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Social media sentiment analysis for local Kenyan products and

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1. Test posts DataAccessService entityListFromHQL from FbPost f where f searchlItem WATER and f classified N size HibernateUtil getCurrentSession flush posts clear posts DataAccessService entityListFromHQL from FbPost f where f searchItem keyWord System out println KEy word keyWord System out println Number of posts posts size setShowGraph false setPositive 0 setNegative 0 setNeutral 0 for PersistentDataEntity post posts if FbPost post getPolarity equalsIgnoreCase P positivett else Lf FbPost post getPolarity equalsIgnoreCase N negativett else Jf FbPost post getPolarity equalsIgnoreCase W neutraltt public void runPythonScript 66 try String line String argsToPythonInterpreter cmd ic C fb_ classifier py Process wanyama Runtime getRuntime exec argsToPythonInterpreter BufferedReader bri new BufferedReader new InputStreamReader wanyama getInputStream BufferedReader bre new BufferedReader new InputStreamReader wanyama getErrorStream while line bri readLine null System out printin line bri close while line bre readLine null System out printin line bre close wanyama waitFor System out println Done catch Exception err err printStackTrace public v
2. UNIVERSITY OF NAIROBI SCHOOL OF COMPUTING AND INFORMATICS SOCIAL MEDIA SENTIMENT ANALYSIS FOR LOCAL KENYAN PRODUCTS AND SERVICES EDWIN WANYAMA NGERO P58 72924 2009 JULY 2012 Submitted in partial fulfillment of the requirements of the degree of Master of Science in Computer Science DECLARATION This project as presented in this report is my original work and has not been presented for any other university award Edwin Wanyama Ngero P58 72924 2009 Sioned oz oen This project has been submitted as part fulfillment of the requirement for the award of Masters of Science in Computer Science of the School of Computing and Informatics of the University of Nairobi with my approval as the University Supervisor Prof Peter W Wagacha SIBTIGUE ctio sae inesi Br c E Dedication I dedicate this work to the memory of my lovely parents who inspired encouraged and provided for my education This dedication also extends to my wife for the support during my project work ii ACKNOWLEDGEMENT I take this opportunity to thank the School of Computing and Informatics University of Nairobi for giving me a chance to pursue and successfully complete this course My sincere thanks go to my supervisor Prof Peter Wagacha and the other members of staff in their various capacities for the untiring support guidance and concern throughout my project work I also thank my course mates for the encouragements and team work we have sh
3. BR localhost eA http localhost 808 emplatesflogin faces x J Fast screen capture tool Google Search template px fr c gt fast capture f B Most Visited Customize Links Windows Marketplace s Sentiment Analyzer Sentiment Analyzer ill y SYSTEM ADMINISTRATION ANALYZER CREATE USER MODIFY USER DETAILS CHANGE PASSWORD LOGOUT SEARCH USER 2012 Edwin Wanyama UI3 System Administration Menu and Menu Items 81 Create User The following are the steps to be followed in creating a new user in the system 1 2 6 7 Mozilla Firefox File Edit View History Bookmarks Tools Help 8 Login to the system in case you are not logged Navigate to the SYSTEM ADMINISTRATION menu and select CREATE USER submenu as shown above System will respond by displaying interface UI4 for use in capturing new user details Capture user details as labeled on the interface then click on the Save button to save the new user s details System saves new user record and creates user id and password that are displayed to the user Send the login credentials to the new user To exit from this interface click on the Cancel button j http flocalhost 8087 es successLogin faces st localhost 8087 icloud templates successLogin Faces E SYSTEM ADMINISTRAT
4. pos_word_count BigramAssocMeasures chi_sq label neg_word_count BigramAssocMeasures chi_sq label neu_word_count total_word_count _word_fd neg word total_word_count _word_fd neu word total_word_count word_scores word pos_score neg_score neu_score best sorted word_scores iteritems key lambda w S S reverse True 1950 bestwords set w for w s in best def best word feats words return dict word True for word in words if word in bestwords cleannegfeats best word feats cleanreader words fileids f neg for f in cleannegids cleanposfeats best word feats cleanreader words fileids f pos for f in cleanposids cleanneufeats best word feats cleanreader words fileids f neu for f in cleanneuids cleannegcutoff n cleannegfeats 3 4 cleanposcutoff len cleanposfeats 3 4 cleanneucutoff n cleanneufeats 3 4 trainfeats cleanposfeats testfeats cleanposfeats print train on len testfeats d instances cleannegfeats cleannegcutoff cleanposcutoff cleanneufeats cleanneucutoff cleannegfeats cleannegcutoff cleanposcutoff cleanneufeats cleanneucutoff o test on d instances classifier NaiveBayesClassifier train trainfeats refsets collections defaultdict set 61 len trainfeats testsets collections defaultdic
5. Exception Paths EP If actor has not entered user id a System informs user Invalid Username b System functionality remain unavailable to the user c System logs the failed login attempt If user id or password provided is invalid a System notifies User with a message Wrong Username or Password b System logs the failed login attempt c System functionality remain unavailable to the user If actor has not entered password System notifies user Wrong Username or Password If actor s account is suspended a System informs actor User account is suspended please contact administrator b System functionality remains unavailable to the User Includes None Priority High Frequency of use Medium Business Rules UC 001 BR1 Login validation 1 Invalid users will not have access to the system 2 The access to the functionalities of the system will be restricted to user authentication via user name and password Special None Requirements Assumptions None Process Owner Research Team 35 Notes and Issues None 4 1 3 2 Create User Use Case ID UC 002 Use Case Name Create User Version Version Created By Date Last Updated Date Last No Created By Updated 1 0 E Wanyama 1 Apr 2012 E Wanyama 1 Apr 2012 Actors Super User Sentiment Analyzer System Description Create User f
6. and support vector machines SVM His results indicate that standard machine learning techniques definitively outperform human produced baselines However he found that machine learning methods could not perform as well on sentiment classification as on traditional topic based categorization Their results indicated that use of machine learning methods is more superior to the use of human generated baselines The advantage is that reviews from such sites already have a clearly specified topic which is often assumed that the sentiments expressed in the reviews have to do with the topic Yessenov and Misailovic 2009 also carried out sentiment analysis on movie review data comments from the popular social network Digg as their data set and classified text by subjectivity objectivity and negative positive attitude They used different approaches in extracting text features such as bag of words model use of large movie reviews corpus restricting to adjectives and adverbs handling negations bounding word frequencies by a threshold and using WordNet synonyms knowledge They then evaluated their effect on accuracy of four machine learning methods Naive Bayes Decision Trees Maximum Entropy and K Means clustering Their results showed that simple bag of words model performed relatively well and could be further refined by the choice of features based on syntactic and semantic information from the text 13 Pak et al 2009 used emoticons t
7. createQuery select distinct MONTH f createdTime from FbPost f where searchItem order by MONTH f createdTime asc ArrayList lt Integer gt datesList new ArrayList lt Integer gt for Iterator it datesQuery iterate it hasNext datesList add Integer it next System out println Number of days E datesList size for Integer d datesList c new ChartData c setDay d toString c setPositive DataAccessService entityListFromHQL from FbPost f where MONTH f createdTime d and f polarity P and f searchItem searchlItem size c setNegative DataAccessService entityListFromHQL from FbPost f where MONTH f createdTime d and f polarity N and f searchItem searchItem size c setNeutral DataAccessService entityListFromHOL from FbPost f where MONTH f createdTime d and f polarity W and f searchItem searchItem size data add c System out println iaacane ws day c getDay Ad TI positive c getPositive ToU lesus negative c getNegative To Ukeni neutral gt c getNeutral return data package ics wanyama analyzer import ics common data PersistentDataEntity public class ChartData extends PersistentDataEntity private String day private Integer positive private Integer negative private Integer neutral public C
8. password that do not match New Password and Confirm Password do not match please try again and Confirm Password do not match please try again Click on Cancel button while in the midst of making changes System should exit the modify user details interface without saving changes that have been done System exits the interface without saving changes 95 v Fetch Data Test Test case Test case Test steps caseid name description Steps Expected Actual Outco me TC 5 Fetch Data To verify that Activate System System Test Case the Fetch Data module by should display displays Fetch functionality clicking on Fetch data data Form works as per the Fetch Data Form interface design submenu interface which is under the Analyzer menu Enter System System keyword and should inform informs user to number of user to wait as wait as it days then it searches for searches for click on posts that posts that have Search have the the specified button specified keyword s keyword s System System displays displays results if results if matching data matching data is found is found Click on System System Search should ask demands for button user to search without provide keyword specifying search keyword keyword Change value System System adjusts of maximum should adjust the number of records to be the n
9. 36 message the national id no already exists in the system Includes UC 001 Priority Medium Frequency of use Medium Business Rules UC 002 BR1 User details to be captured i National ID Number ii Profile iii Surname iv Other Names v Status Active or Disabled vi Contact details Email address physical address postal address postal code Telephone and mobile number UC 002 BR2 National Id Number i Length must be 8 characters ii Must be numeric Special None Requirements Assumptions None Process Owner Research Team Notes and Issues The user interface shall provide input validation checks to ensure capture of valid data 4 1 3 3 Modify User details Use Case ID UC 003 Use Case Name Modify User details Version Version Created By Date Created Last Updated Date Last No By Updated 1 0 E Wanyama 1 Apr 2012 E Wanyama 1 Apr 2012 Actors Super User Sentiment Analyzer System Description Edit User functionality allows the actor associated with the use case to modify details of an existing user Preconditions The actor has been appointed The actor s profile has the function to edit user The actor has been authenticated and has access to the system The list of existing users is available via the user interface The Modify User details function is available
10. Facebook and Twitter API interface modules li Classification Module ili Reports Module iv User administration The research employed Use Cases to describe system actor interactions A use case is a sequence of actions that provide a measurable value to an actor Another way to look at it is a use case describes a way in which a real world actor interacts with the system The overall goal of the system is to classify Kenyan sentiments obtained from Facebook and Twitter into three categories positive negative or neutral It therefore has to provide interface for searching and fetching relevant Facebook and Twitter data Diagrammatically the system Use Case Model is as shown in figure below 32 uc Use Case Model Sentiment Analyzer System Boundary Modify User Record 3 Change Password Fetch Data Classify Data iew Classified Data Figure 5 Use Case Model 33 4 1 2 Actors Two actors are involved namely i TheSystem This refers to the Sentiment Analyzer System which constitutes various modules interconnected to provide various functionalities that fulfill the objectives of this research ii User of the system This is the human actor interested in benefiting from the utilities offered by the System functions 4 1 3 Use Cases 4 1 3 1 Login Authenticate User Use Case ID UC 001 Use Case Name Authenticate User Version Version Created By Date Last Updated Da
11. Password interface works correctly Password interface submenu Enter old System System password should acknowledges new password acknowledge successful and confirm successful change of new password change of password via then click on password via password Change password changed Password changed successfully button successfully message to the message to user the user Enter new System System gives password that should give message is less than 8 message Passwords must characters Passwords contain a must contain a minimum of 8 94 minimum of 8 characters and must contain at least one characters and must contain at least one digit and character digit and character Enter new System System gives password that should give message does not message Passwords must contain a digit Passwords contain a must contain a minimum of 8 characters and must contain at least one minimum of 8 characters and must contain at least one digit and character digit and character Enter new System System confirms password that should successful save does not confirm operation for the contain an successful changes made on alphabetic save operation the user record letter for the changes made on the user record Enter new System System gives password and should give message The confirmation message The New Password
12. TODO Auto generated catch block e printStackTrace private PieDataset createSampleDataset int positive int negative int neutral final DefaultPieDataset result new DefaultPieDataset result setValue Negative negative result setValue Neutral neutral result setValue Positive positive return result public int getPositive return positive public void setPositive int positive this positive positive public int getNegative return negative public void setNegative int negative this negative negative public int getNeutral return neutral public void setNeutral int neutral this neutral neutral public void setAccessToken String accessToken this accessToken accessToken public String getAccessToken return accessToken public void setDays int days this days days public int getDays 68 return days public String getKeyWord return keyWord public void setKeyWord String keyWord this keyWord keyWord public int getNumberOfPosts return this getPosts size public void setSearchCriteria ArrayList lt SelectItem gt searchCriteria this searchCriteria searchCriteria public ArrayList lt SelectItem gt getSearchCriteria return searchCriteria public void setShowGraph boolean showGraph this showGraph showGraph public boolean isShowGraph return showGraph private List lt ChartData gt
13. allows message sizes of up to 60000 characters All tweets fetched were stored since they don t exceed 140 characters For each post fetched we stored the following attributes i The unique post identifier li Post message il The time the post message was created iv The item searched for v The source of the message i e whether Facebook or Twitter vi Polarity which is null as at the point of fetching the data This field is later updated during classification of the message s polarity 28 3 5 3 Data Preparation for Classifier training The raw data harnessed from Facebook and Twitter is directly unsuitable for use in training a polarity classifier since it often has unwanted characters such as links exclamation marks question marks and other irrelevant characters These characters are not of essence to this study except for emoticons that are used as means of expression The entire corpus stored in the MySQL database was then picked and transferred it to three text files The three files correspond to the three categories of features on which the classifier is to be trained namely positive negative and neutral Time was taken to manually read and clean the contents of each file line by line while removing irrelevant characters words and sentiments The result of this exercise was three files each of which is a collection of sentiments that express one category of polarity We currently have 981 negative sentiments in the file for ne
14. as UII Enter username and password then click on the Submit button to login To clear the username and password fields of entered data click on Clear button 4 If login credentials are correct system presents interface UI2 which is the Main Form See interface below 5 The main menus and their respective sub menus are visible on interface UI2 UI3 and UI8 Mozilla Firefox BAE File Edit View History Bookmarks Tools Help M Gmail Inbox 133 wanyama edwin g http flocalhost 808 s successLogin faces les screen capture tool Google Search wj AR localhost 8087 icloud templates successlogin Faces C 84 Fast capture Bic Visited Customize Links Windows Marketplace s Sentiment Analyzer a Sentiment Analyzer uj y Forgot Password 2012 Edwin Wanyama 7 Mozilla Firefox UII Login Form 80 3 Mozilla Firefox File Edit View History Bookmarks Tools Help E http ilocalhost 8087 ftemplates login faces SS localhost ogin Fai ustomize Links Windows Marketplace a Sentiment Analyzer Sentiment Analyzer SYSTEM ADMINISTRATION ANALYZER LOGOUT 2012 Edwin Wanyama UD Main Form la Firefox File Edit View History Bookmarks Tools Help IM Gmail Inbox 133 wanyama edwin g
15. corpus 52 Table 5 Effect of removing low information gain features on balanced corpus 53 viii List of Figures Figure 1 User age Distribution on Facebook in Kenya Source Socialbakers 2012 9 Figure 2 Male Female User Ratio on Facebook in Kenya Source Socialbakers 2012 10 Figure 3 Conceptual Model oto etr dt etsi qe riae p ge neiii orte Egs EES 2l Figure 4 Supervised classifier Model 5 ice intet e pedet cea tra Letti eere eaa 25 Figure 5 Us C s Model sirsie iina a tel eatin Monte E pei 33 Figure 6 Sentiment Analyzer System Design eee 46 Figure 7 Sentiment Analyzer System Data Model see 49 Figure 8 Summary of Methodology Used eee 50 1X Definitions of terms Your Wall is where you and your friends can write on your Profile Your friends may write on your Wall to communicate with you congratulate you embarrass you and more You post on your own Wall to let your friends know what you re up to The News Feed is a continuous stream of updates about your friends activities on and off Facebook It appears on your Home page A post is a message posted on ones wall It s also called a status update One may specify it as publicly accessible or restrict its accessibility by specifying it as private Facebook provides a maximum length of 60 000 characters for a status update A Friend is someone you are connected to on F
16. difficult for an individual to autonomously control the unveiling and dissemination of data about his her private life Wel amp Royakkers 2004 Sentiment analysis or opinion mining involves the use of personal data of some kind and can lead to the disruption of some important normative values One of the most obvious ethical objections lies in the possible violation of peoples informational privacy Protecting the privacy of users of the Internet is an important issue Informational privacy mainly concerns the control of information about oneself It refers to the ability of the individual to protect information about oneself The privacy can be violated when information concerning an individual is obtained used or disseminated especially if this occurs without their knowledge or consent Wel and Royakkers note that the privacy issues due to web mining often fall within this category of informational privacy They further state that the value of peoples individualism is violated when they are judged and treated based on patterns resulting from web data mining When data obtained from the web is made anonymous the discovered information no longer links to individual persons and there is no direct sense of privacy violation because the data is not then directly linked to a person This study intends to use data from Facebook and Twitter that is specified as public by the users who generated it The data will be made anonymous to protect the
17. of inadequate literature on it since it is informal This made it challenging during preparation of training data and also during classification We were challenged in finding a way of filtering data from Facebook to Kenyan content since Facebook restricts running of queries with joins Consequently some of the keywords searched for would fetch sentiments done in irrelevant and unknown languages We were also limited to 7 days and 14 days at most in terms of how far back the search results would go for Twitter and Facebook respectively Future Work The following improvements can be done on the Sentiment Analyzer System l Functionalities for accommodating other classifiers other than the Naive Bayes classifier can be developed into the application These classifiers include Decision trees and Support Vector Machines Results from the various classifiers can be compared in a report interface for the best classification technique to be selected Training on multiple words can also be explored to resolve the limitation 3 faced in section 6 2 above The application can also be enhanced to be accessible on handheld devices such as mobile phones Integration with multi and cross lingual language dictionaries to cater for the dynamic nature of language used on social media 57 References Abbasi A Chen H and Salem A 2007 Sentiment Analysis in Multiple Languages Feature Selection for Opinion Classification in Web Forums A
18. of informal opinionated texts like blog posts and product review websites a field of Sentiment Analysis has sprung up in the past decade to find out what people feel about a certain issue According to socialtimes 2011 researchers at eBay research labs expressed that properly conducted sentiment analysis provides more usable information than surveys and focus groups where participants may want to complete the process quickly or express less than honest opinions due to the influence of others Sentiment Analysis is an active area of ongoing research It is a field of study that aims to uncover the attitude of the author on a particular topic from the written text 12 2 2 1 Examples of Sentiment Research Sentiment analysis has been handled as a Natural Language Processing task at many levels of granularity Starting from being a document level classification task Pang and Lee 2004 it has been handled at the sentence level Hu and Liu 2004 and more recently at the phrase level Wilson et al 2005 Agarwal et al 2009 Most research work in Sentiment analysis work has been done on product and movie reviews where it is easy to identify the topic of the text This is because of the ready availability of product review datasets Mejova 2009 Pang et al 2002 conducted an extensive experiment on movie reviews using three traditional supervised machine learning methods i e Naive Bayes NB maximum entropy classification ME
19. privacy of individuals by picking attributes of posts and tweets that exclude the author s identity We will therefore collect public messages the corresponding message identifiers and the time the 19 messages were created for purposes of this study Facebook clarifies on its privacy statement that information made public on its site can be viewed by anyone even off Facebook Such information also shows up when someone does a search on Facebook or on a public search engine The information can also be accessed by the games applications and websites one visits with friends and is also accessible to anyone who uses its APIs such as the Graph API 2 6 Conceptual Model Based on the insight obtained from the relevant literature cited above this paper draws upon three approaches i Pattern based approach where we use the unigram model of features ii Simple Bag of words model with information gain heuristic iii Machine Learning where we use the Naive Bayes technique These three approaches inform the creation of a conceptual model shown in Figure that is used in this research work 20 Social Media Data on the Web Data Store Preprocessing m Feature Extraction using o Classifier Training Simple Bag of Words and Information Gain Heuristic Classifier Model Opinion Sentiment Polarity Decision Making Figure 3 Conceptual Model 21 CHAPTER 3 3 0 Methodology This chapter presents the resea
20. technology for their own purposes as well as those of their communities In order to promote the harnessing of collective knowledge from people websites must be easy enough to use that they don t stand in the way of people using the Internet to share their knowledge Thus whereas Web 2 0 is about creating a social web it is also about creating a more interactive and responsive web It is in this way that technologies such as AJAX RSS and 10 Eclipse become central to the idea of Web 2 0 It is worth mentioning that though many social media sites were built and run on different technologies they all provide for these technologies to enrich user experience AJAX which stands for Asynchronous JavaScript And XML allows websites to communicate with the browser behind the scenes and without human interaction This means you don t have to click on something for the web page to do something RSS which stands for RDF Site Summary is an XML based vocabulary for distributing Web content in opt in feeds Feeds allow the user to have new content delivered to a computer or mobile device as soon as it is published An RSS aggregator or RSS reader allows the user to see summaries of all their feeds in one place Instead of visiting multiple Web pages to check for new content the user can look at the summaries and choose which sites to visit for the full versions Eclipse is a Java based open source platform that allows a software developer t
21. the all the individual individual messages in colors messages in colors 98 corresponding corresponding to their to their determined determined polarity polarity Click on System System View Pie should display displays a pie Chart button a summary of chart showing whensearch the classification phrase is still classification summary in specified for the chosen terms of phrase in a pie percentage chart Click on System System View Line should display displays a Line graph button a Line graph graph of the when search of the classification phrase is still classification for the chosen specified for the chosen phrase User phrase for can view each polarity trends for the type three polarities Click on System System exits Cancel should exit and takes button to exit and take focus focus to Main the View to Main Form Form when Results Form when Cancel Cancel button button is is clicked clicked 99
22. trendlData new ArrayList ChartData public static JasperPrint jasperPrint private String rootPath UseMessageBundle getMessageResourceString FacesContext getCurrentInstance getApplication getMessageBundle application root physical path null FacesContext getCurrentInstance getViewRoot getLocale public void generateLineGraph trendlData generateData 2012 this getKeyWord JRBeanCollectionDataSource beanCollectionDataSource new JRBeanCollectionDataSource trendlData try jasperPrint JasperFillManager fillReport rootPath concat analyzer line graph jasper new HashMap beanCollectionDataSource catch JRException e TODO Auto generated catch block e printStackTrace JRXhtmlExporter xhtmlExporter new JRXhtmlExporter xhtmlExporter setParameter JRExporterParameter JASPER_PRINT jasperPrint xhtmlExporter setParameter JRExporterParameter OUTPUT FILE NAME rootPath concat analyzer lineGraph xhtml xhtmlExporter setParameter JRExporterParameter IGNORE PAGE MARGINS r ue try xhtmlExporter exportReport catch JRException e e printStackTrace 69 public List lt ChartData gt generateData int year String searchItem f searchItem ChartData java List lt ChartData gt data new ArrayList ChartData ChartData c int totalNumber 1000 int datal 0 Query datesQuery DataAccessService session
23. web scale processing with all the attendant problems of pre processing spelling grammar mistakes broken HTML etc brings in a whole set of other problems Data sets are in the hundreds of thousands sometimes millions and are neither clean nor consistent Algorithms need to be efficient and subtle language effects become visible The second drawback has to do with the feature model employed i e Unigram versus N gram Current work mostly focuses on using unigrams and bigrams rather than higher order n gram models to capture sentiments in the text They considered that that those experiments were hindered by the small training corpora and thus were not able to show the effectiveness of high order n grams n _ 3 in discerning subtleties in expressing sentiments To remedy these problems they made three contributions in their research work First they conducted experiments on a corpus of over 200k online reviews with an average length of 14 over 800 bytes crawled from the Web Such a large scale data set allowed them not only to train language models and cull high order n grams as features but also to study the effectiveness and robustness of classifiers in a simulating context of the Web Secondly they studied the impact of higher order n grams n _ 3 Compared to previous published work they showed that there is a benefit to use higher order n grams beyond unigrams and bi grams This was done alongside other experiments that employ
24. words Model Train a supervised Classifier and embed it in a web based application Use trained classifier to predict polarity of Features from given input i e Perform classification and note results Evaluation of Results Accuracy Precision amp Recall Conclusion Figure 8 Summary of Methodology Used 50 CHAPTER 5 5 0 Results and Findings In this section we present the results of our experimentation in this research work We performed two sets of tests one set for checking the functionalities of the entire application which was done using the Test Cases 1 to vii given under appendix 3 while the other was done with the Naive Bayes Classifier In experimenting with the Naive Bayes Classifier we relied on the NLTK metrics module which provides functions for calculating accuracy precision and recall for the Classifier 5 1 Test runs and Presentation of Results We performed a number of tests whose results are as tabulated below 5 1 1 Test runs with Naive Bayes Classifier 5 1 1 1 Experiments with unbalanced corpus i Effect of stop words Experiments with unbalanced corpus negative set 1036 words neutral set 367 words positive set 610 words without removing low information gain features No Experiment Classifier Pos Pos Neg Neg Neu Neu Accuracy Precision Recall Precision Recall Precision Recall 1 Classification with stop 11 2 63 7
25. 11 9 87 0 92 2 71 7 35 9 words 2 Classification without stop 76 4 62 6 77 9 87 8 91 1 65 3 34 8 words Table 2 Effect of stop words on unbalanced corpus 51 ii Effect of low information gain features without removing stop words Experiments with unbalanced corpus negative set 1036 words neutral set 367 words positive set 610 words when low information gain features are removed No Experiment Classifier Pos Pos Neg Neg Neu Neu Accuracy Precision Recall Precision Recall Precision Recall 1 With best 500 words 77 6 66 4 69 3 83 0 95 5 79 6 42 4 2 With best 1000 words 77 8 67 1 71 4 83 3 95 1 79 2 41 3 3 With best 1500 words 78 2 65 6 73 6 85 8 95 5 76 6 39 1 4 With best 1950 words 78 9 68 2 73 6 85 1 95 5 76 6 40 9 5 With best 2000 words 78 2 65 6 73 6 85 8 95 5 76 6 39 1 Table 3 Effect of removing low information gain features with stop words retained 5 1 1 2 Experiments with balanced corpus i Effect of stop words Experiments with balanced corpus negative set 350 words neutral set 350 words positive set 350 words without removing low information gain features No Experiment Classifier Pos Pos Neg Neg Neu Neu Accuracy Precision Recall Precision Recall Precision Recall 1 Classification with stop 54 5 4
26. 4 8 87 5 73 3 37 5 72 3 38 6 words 2 Classification without stop 53 8 45 3 83 0 73 2 34 1 62 9 44 3 words Table 4 Effect of stop words on balanced corpus 52 ii Effect of low information gain features without removing stop words Experiments with balanced corpus negative set 350 words neutral set 350 words positive set 350 words when low information gain features are removed No Experiment Classifier Pos Pos Neg Neg Neu Neu Accuracy Precision Recall Precision Recall Precision Recall 1 With best 500 words 58 7 46 0 84 1 86 1 35 2 74 6 56 8 2 With best 700 words 58 7 46 0 84 1 86 1 35 2 74 6 56 8 3 With best 800 words 58 7 46 0 84 1 86 1 35 2 74 6 56 8 4 With best 900 words 59 5 46 9 85 2 82 5 37 5 76 6 55 7 5 With best 1000 words 61 0 48 1 85 2 84 1 42 0 16 6 55 7 6 With best 1050 words 61 4 48 1 85 2 84 8 44 3 77 4 54 5 Table 5 Effect of removing low information gain features on balanced corpus 5 1 2 Sentiment Analyzer System results The sentiment analyzer system was tested using test cases see appendix 3 All the system features were observed to work as stipulated in the design 53 5 2 Evaluation of Results The results presented in part i of section 4 1 1 1 above indicate that removal of stop words lowers the performance of the classifier on all the me
27. AL _ADDRESS VARCHAR 25 ID INT 11 CODE INT 11 MODULE NAME VARCHAR SO CREATION OFFICER VARCHAR 50 CREATION DATE DATE 7 ID INT 11 CODE VARCHAR 3 gt PROFILE NAME V ARCHAR SO gt PROFILE STATUS INT 11 CREATION DATE DATE Lue CREATIOM OFFICER VARCHAR SO UPDATING OFFICER V ARCHAR SQ UPDATING DATE DATE 7 sa system user ID INT 11 L p v post id VARCHAR SO 7 id INT 11 VALUE VARCHAR 200 post message TEXT classified CHAR 1 polarity CHAR 1 created time VARCHAR 50 search item V ARCHAR 45 source CHAR 1 CODE VARCHAR 20 Figure 7 Sentiment Analyzer System Data Model 49 ID INT 11 CODE INT 11 MODULE _CODE INT 11 FUNCTION NAME VARCHAR SO ACCESS INTERFACE PAGE VARCHAR SO gt CREATION OFFICER V ARCHAR 15 CREATION DATE DATE UPDATING OFFICER V ARCHAR 15 UPDATING DATE DATE FN CATEGORY CHAR 3 7 sa system module ID INT 11 ID INT 11 gt PROFILE ID INT 11 Q SYSTEM FUNCTION ID INT 11 7 sa system profile ID INT 11 7 sa system function ID INT 11 7 sa system function sa system module ID INT 11 gt 4 4Summary of Methodology Social Media Customize Social Media APIs Store Data Collected from Social Media Data sanitization and Labelling into Positive negative and Neutral Extract Features using Simple bag of
28. EARCH USER Search Criteria Surname weke Other Names National Id Number User Name Search Reset List of Users Choose UserName Staff No Surname Othernames Email K10020033 10020033 Wekesa David wekesa gmail com 4 1 2012 Edwin Wanyama http localhost 8087 icloud templates successLogin faces start fam 3 WindowsE M 2 Microsoft O hd 2 Firefox 5 MyEdipse Java pe pythonw UI5 Modify User Details Form 83 Change Password To change your password do the following 1 While logged in navigate to the SYSTEM ADMINISTRATION menu and select the CHANGE password submenu 2 System responds by displaying interface UI6 Change Password Form 3 Enter old password new password and confirm the new password then click on Change Password to effect the changes 4 To exit the interface click on Cancel Mozilla Firefox File Edit View History Bookmarks Tools Help a http localhost 8087 rchInternalUserfaces B FR localhost 8087 jicloud sa searchinternalUser face ela F i SYSTEM ADMINISTRATION ANALYZER LOGOUT 2012 Edwin Wanyama UI6 Change Password Form Search User To search for a user existing in the system do the following 1 While logged in the system navigate to the SYSTEM ADMINISTRATION menu and select the SEARCH USER submenu 2 System responds by displaying interface UI7 Sear
29. Facebook Graph API Twitter API and the developed classifier to provide functionality for extracting classifying and presenting classification results of data obtained from social media in a manner that can give a user with summarized as well as detailed view of customer opinion about a service or product We are able to achieve a relatively good accuracy of 78 9 with a modest dataset iv Table of Contents DECLARATION i i oitensnsstasdisco tied eie dsdien ie iv Nb pv lot wee iunt i Dera EIES 3 SEAN oe BEE C Mec d ECCE ii Eist ot Tables 5d iet a e a dus ils code E E A Dem cde AE ident viii Last Df AH BUEE SS nanna a a a E EA ix Definitions of terms moona E a ee eee O RRE ode X Bir Eddie U OUe 1 Wt Gu COON D 1 el Background to the SUdy siii does DEOS Dti ventas des oc UO OM VUE QUNM RA 1 1 2 Problem Statement note dre Dii e eoe nee 2 1 3 SODJeCU VES secs hohe et gu ie a nie do added ti uoo de S onde es 3 14 Re search Ouestionss 5 eee Ee ES 4 1 5 Research outcomes and their significance to key audiences 4 1 6 Assumptions Limitations of the Research eeesseeeeereenene 4 CHAPTER 2 5 atu e est READ UR ed e i iua RR asennad 5 2 0 3torature REVIEW o sse nitet aset ye tats e dba isonte ota deg Meads eS in ss S ah 5 21 Social UL 5 2 1 1 Examples of Social Media Websites eei c tti enero rote t Rte Ree in 6 2 1 2 Gr
30. ION ANALYZER General Information National ID Number Surname Status ACTIVE Contacts Physical Address Code Telephone Email Address UI4 Create User Form Save USER DETAILS Profile Other Names P O Box City Mobile Confirm Email Address Cancel 82 LOGOUT Modify User Details To modify a user s details in the system do the following 1 While logged into the system navigate to the SYSTEM ADMINISTRATION menu and select the MODIFY USER DETAILS submenu 2 System displays interface UI7 Search User Form Enter search criteria then click Search button to locate the user of interest 4 System will respond with a list of users meeting search criteria provided otherwise no results will be returned 5 Select the user record of interest by clicking on the Choose icon on the extreme left column of the record 6 System will respond with a display of the user details in edit mode The display is same as UIA create User Form 7 Make changes to the user record and click on Save button to persist the changes 8 Click on Cancel button to exit Mozilla Firefox Sa File Edt View History Bookmarks Tools Help 3 locathost 8087 icloud templates successLoain Faces e ps e http jlocalhost 8087 es successLogin fFaces Sentiment Analyzer SYSTEM ADMINISTRATION ANALYZER LOGOUT S
31. L Server Database Installation Instructions Download and install MySQL database software NB Leave user root without password during installation Download and install HeidiSQL software to help in deploying managing databse objects Navigate to folder named Python tools on installation CD You see a number of tools to be installed Double click MySQL python 1 2 3c1 win32 py2 6 to install the Python MySQL connector Follow instructions as prompted by the Wizard to instal to completion 78 e Launch HeidiSQL and login as user root then ensure you are in test database and open the Query tab Open and Copy database scripts file from installation CD then paste them into the Heidi Query window and execute run to deploy database objects Sentiment Analyzer System Installation Instructions Copy folder named wanyama from CD and paste it under webapps folder in apache tomcat installation directory Restart Apache tomcat application server The restart ensures application is deployed 79 Appendix 3 User manual for sentiment Analyzer System Login The following are steps to be followed during login to the application 1 Launch your browser and enter URL given below and press enter http localhost 8087 icloud templates login faces if installed on the same computer or http XXXX 8087 icloud templates login faces where XXXX is the IP of the application server 2 System will present login form shown
32. Sentiment classifier that is able to classify sentiments in the stored Social Media data into positive negative or neutral 4 Embed the classifier developed in a web based application for use in performing Social Media sentiment analysis on local Kenyan products and services 1 4 Research Questions l 2 3 4 What are the issues involved in harvesting data from Social Media What is needed in harvesting data from Social Media on various products and services offered in Kenya Are there suitable cost effective techniques that can be used for this task Which attributes of Social Media data can we obtain that are relevant for our study Which classification models have been successfully used before in similar research tasks What are some of the issues that surround the usage of these models that would inform our selection of the most suitable model for use in carrying out this research work What is the best way to integrate the techniques learned and developed into an application that can provide Social Media sentiment analysis from the point of data collection to presentation of results Which development platform renders itself suitable for integration of some of the heterogeneous tools that might be used 1 5 Research outcomes and their significance to key audiences The study is significant because it will involve work that will lead to l 2 3 A sentiment analyzer algorithm that can be used on Kenyan issues A web b
33. Twitter and Facebook Natural language toolkit of Python 22 programming language was used in implementing the classifier 3 Provide a tool that performs We developed a web based application that provides Sentiment Analysis for interfaces for use in searching and fetching Kenyan issues based on the Facebook data It then uses the classifier that was classifier developed developed and trained to performing Sentiment Analysis on entities topics in Kenya Table 1 Activities carried out to achieve objectives 3 1 Research design This research design was chosen because of the sparse and informal nature of Social Media data a scenario that necessitates use of machine learning and computational linguistics techniques Traditional approaches of natural language processing are challenged by the lack of language formality usage on Social Media 3 2 Data Collection Methods and Tools The source of data for this study is Facebook and Twitter This data includes text emoticons and acronyms Facebook and Twitter provide millions of users an opportunity to express their personal opinions about any topics Thus the data available on this site and other social network sites has immediate applicability in business environments such as gaining information from summarized views of users on certain products or services Yessenov and Misailovic 2009 Data for this study was collected from Facebook and Twitter
34. able open source community that offers support It provides interactive evaluation that supports learning and rapid development It is broadly used in scientific research A similar view is held that NLTK was designed with four primary goals in mind Bird et al 2009 Simplicity To provide an intuitive framework along with substantial building blocks giving users a practical knowledge of NLP without getting bogged down in the tedious house keeping usually associated with processing annotated language data Consistency To provide a uniform framework with consistent interfaces and data structures and easily guessable method names Extensibility To provide a structure into which new software modules can be easily accommodated including alternative implementations and competing approaches to the same task Modularity To provide components that can be used independently without needing to understand the rest of the toolkit 26 3 3 Implementation of the Sentiment Analysis Tool in a web based application The trained and tested classifier was used to develop a sentiment analyzer that was implemented in a web based application for use in Kenya The application is intended to provide ratings on various topics 3 4 Evaluation The following criteria were used to evaluate the overall system upon completion of development A positive answer to each of the questions below confirmed the success of this research i Is the tool able to c
35. acebook Friending is the act of sending someone a friend request All friendships have to be confirmed by both people in order for it to be official on Facebook Your Profile is your page It contains your Wall your photos and videos a list of your friends your favorite activities and interests and anything else you choose to share A Poke is a casual gesture that means I m thinking of you The person you poke receives the Poke on her Home page when she logs in and has the choice to poke you back The Poke is only visible to the person Poked Corpus is a large and structured collection of texts or writings that are usually electronically stored and processed Social Network is a social structure made up of persons called nodes which are tied or connected by one or more specific types of interdependency such as friendship kinship common interest financial exchange dislike sexual relationships or relationships of beliefs knowledge or prestige A tweet is an update message posted on ones profile on twitter A tweet is limited to 140 characters xi CHAPTER 1 Introduction 1 1 Background to the study The development and use of social network sites such as Facebook twitter and Linked In have revolutionized the way most people in the current world share information and keep in touch with friends People are able to express their opinions in form of posts emoticons with regard to many issues that affect their day to da
36. acebookClient facebookClient private List lt FbPost gt fetchedPosts new ArrayList FbPost private String keyWord private int days 1 public static void main String args new FbUtility AAACEdEOSeO0CBAJOJUALxZBIWGTRIDOCmpGNGvEhEwUXzx3uRHfpgSnUmw7qrd6EfOv MVdPwKa7ZB9lgyTevU1FGwA38ablNe6d5jmCyunntpgDHOQIO 1 runEverything FbhUtility String accessToken String key word this keyWord key_word this days entered_days water int entered_days facebookClient new DefaultFacebookClient accessToken void runEverything search selection parameters rawJsonResponse void search fetchedPosts clear Date periodStart addDaysFromDate new Date 0 days Connection lt Post gt publicSearch facebookClient fetchConnection search Post class Parameter with q this keyWord Parameter with type post Parameter with since periodStart Parameter with limit 100 Parameter with locale en US 5 for Post p publicSearch getData FbPost fbp new FbPost fbp setPostId p getId fbp setMessage p getMessage fbp setCreatedTime p getCreatedTime fbp setClassified N fbp setPolarity null fbp setSource F fbp setSearchItem keyWord replace c if fbp getMessage fbp getMessage length lt 200 fetchedPosts add fbp out printin Number of fetched fet
37. aceted Problem Available at http www cs uic edu liub FBS IEEE Intell Sentiment Analysis pdf Last accessed 8th Nov 2011 Mayeku D 2011 Top 10 Social Networking Sites in Kenya December 2011 Available at http www openbook co ke 201 1 top 10 social networking sites in kenya december 2011 html Last accessed 19th Apr 2012 58 Mejova Y 2009 Sentiment Analysis an overview Available at http www divms uiowa edu ymejova publications Comps Y elenaMejova pdf Last accessed 1st Nov 2011 Neil G 2011 Sentiment Analysis and Social Media Marketing Available at http socialtimes com sentiment analysis social media marketingl b58996 Last accessed 8th Nov 2011 Pak A and Paroubek P 2009 Twitter as a Corpus for Sentiment Analysis and Opinion Mining Universit e de Paris Sud Cedex France Pang B Lee L and Vaithyanathan S 2002 Thumbs up Sentiment Classification Using Machine Learning Techniques n Proceedings of the Conference on Empirical Methods in Natural Language Processing EMNLP pp 79 86 Pang B and Lee L 2008 Opinion mining and sentiment analysis Foundation and Trends in Information Retrieval 2 1 2 1 135 Socialbakers 2012 Kenya Facebook Statistics Available at http www socialbakers com facebook statistics kenya chart intervals Last accessed 19th Apr 2012 Wel L and Royakkers L 2004 Ethical Issues in Web data mining Available http alexandria tue nl reposit
38. and removal of stop i words using bag of words Fetch and loop through each message from Db and classify f rore message Update unclassified v record in Db with polarity result Figure 6 Sentiment Analyzer System Design Web The system interacts with the Web in order to fetch data from Facebook and Twitter Facebook and Twitter APIs These APIs provide an interface for interaction between Sentiment Analyzer System and the two social networks Duplicate Checker The purpose of this function in the Data acquisition module is to ensure there is no duplication of messages fetched from Facebook and Twitter It uses the message Ids to enforce this check 46 Data Store The purpose of this module is to store data fetched from Twitter and Facebook into a MySQL database Feature Extraction Module This module does extraction of feature sets which are rendered to the classifier in feature label pairs where the feature is the word and the label is the polarity It also removes stop words which have been found to be insignificant in the classification task It is called into action throughout the classification process Training and Classification Module This module is charged with training a Naive Bayes Classifier based on the features returned by the feature extractor The trained classifier is then used to determine the polarity of sentiments in the database Display Module This module provides visualization
39. and Issues The user interface shall provide input validation checks to ensure capture of valid data 38 4 1 3 4 Search User Use Case ID UC 004 Use Case Name Search User Version Version Created By Date Created Last Updated Date Last No By Updated 1 0 E Wanyama 1 Apr 2012 E Wanyama 1 Apr 2012 Actors User Sentiment Analyzer System Description Search User functionality allows the actor associated with the use case to view a list of users matching specified search criteria Preconditions The actor has been appointed The actor s profile has the function to search user The actor has been authenticated and has access to the system The Search User function is available via the user interface Soo Triggers Selection of Modify User Details or Search User functions from the System Administration menu Basic Course The following shall be the procedure to search for an existing user in the system 1 Actor selects the Search User function from the System Administration menu 2 System displays UIT 3 Actor enters one or more user attributes to be used as search parameters and clicks on Search button to fetch results 4 System displays a list of users whose records match the search parameters provided If no search parameters are provided system displays a list of all users A link for drilling down to u
40. ared during this project Above all great thanks go to God for His providential care all through the time of my study iii Abstract It has become a common practice on the web for a consumer to learn how others like or dislike a product before buying or for a manufacturer or service provider to keep track of customer opinions on its products so as to improve the user satisfaction However as the number of reviews available for any given product or service grows it becomes harder and more difficult for people to understand and evaluate what the majority opinions about the product are In this work we attempt to resolve this problem for customer care service center in a Kenyan context by applying Sentiment analysis on people s opinions expressed on social media Sentiment Analysis or opinion mining attempts to resolve this problem by first presenting the user with an aggregate view of the entire data set summarized by a label or a score and secondly by segmenting the opinions sentiments into three classes positive negative and neutral that can be further explored as desired We began by developing a module for facilitating searching extraction and storage of opinions from social media This was followed by development and training of a polarity classifier using the Python Natural Language Toolkit NLTK where we used the Naive Bayes machine learning technique The study went ahead to develop a web based application that integrates
41. as also used in providing a set of stop words As earlier explained the feature set function checks and removes any appearance of stop words from the sentiments being classified 48 4 3 5Data Model 7 ID INT 11 USERNAME VARCHAR 15 STAFF NO VARCHAR 10 USER STATION VARCHAR S PROFILE ID INT 11 PROFILE ASSIGNING OFFICER V ARCHAR SO PROFILE START DATE DATE CREATION DATE DATE CREATION OFFICER V ARCHAR 15 PASSWORD V ARCHAR S0 PASSWORD CHANGE DATE DATE PASSWORD CHANGE STATUS CHAR 1 PASSWORD EXPIRY DATE DATE USER STATUS CHAR 1 SURNAME V ARCHAR SO OTHER NAMES VARCHAR 50 c ID NUMBER INT 11 PO BOX VARCHAR S0 BOX _CODE INT 11 CITY VARCHAR SO TELEPHONE_NUMBER V ARCHAR 50 MOBILE NUMBER V ARCHAR SO 7 ID INT 11 USERNAME V ARCHAR 15 LOGON _DATE DATE LOGOUT_DATE DATE IP ADDRESS V ARCHAR SO0 7 sa system user ID INT 11 ID INT 11 USERNAME V ARCHAR 15 FAIL LOGON DATE DATE NUMBER CF ATTEMPTS INT 11 IP ADDRESS VARCHAR SO 7 sa system user ID INT 11 7 ID INT 11 9 USERNAME V ARCHAR 13 OLD PASSWORD VARCHAR 50 NEW PASSWORD VARCHAR S0 PASSWORD CHANGE DATE DATE IP ADDRESS V ARCHAR SO0 7 sa system user ID INT 11 EMAIL 1 VARCHAR 80 c EMAIL 2 VARCHAR 80 UPDATING OFFICER VARCHAR S0 O UPDATING DATE DATE PHYSIC
42. ased application that does sentiment analysis for Kenyan issues Providing various players Kenyan economy with ratings as regards consumer perceptions on services and goods given and other issues of concern that should be addressed for purposes of improvement 1 6 Assumptions Limitations of the Research l The words used on Facebook posts and Twitter do not fully constitute formal language They involve acronyms emoticons slang and sheng Due to the limitation of time for this study only small sizes of the corpora will be used in the experiments Most people in Kenyan use more than two languages to express their feelings We will restrict ourselves to text in English Kiswahili and sheng We explore only comments on products and services in Kenyan CHAPTER 2 2 0 Literature Review 2 1 Social Media Social media is an umbrella term that describes websites that connect individuals somehow A hallmark of social media is the user generated content This model contrasts with the editorially controlled style of old media Social media is sometimes called Web 2 0 The best way to define social media is to break it down Media is an instrument on communication like a newspaper or a radio so social media is a social instrument of communication In Web 2 0 terms this would be a website that does not just give you information but interacts with you while giving you that information This interaction can be as simple as asking for yo
43. at an accuracy of 78 996 according to its inbuilt NLTK accuracy module A look at the classified records shows that the classifier is able to determine the polarity of most of the sentiments 3 5 6 Evaluation of Classifier In experimenting with the Naive Bayes Classifier we relied on the NLTK metrics module which provides functions for calculating accuracy precision and recall for the Classifier 30 3 5 7 Implementation of the system The Sentiment Analyzer System was implemented using Java language for the application logic whereas the interface was realized using Java Server Faces JSF MySQL database was used as the back end for storing data fetched from Facebook and Twitter and other application data 3l CHAPTER 4 4 0 Design and Implementation of Sentiment Analyzer System 4 1 System Design Specification In this section we specify the functionalities of the system associated inputs and outputs together with associated data sources We employed the use of agile software development Methodology to guide in the object oriented development of the application software 4 1 1 System Description The Sentiment Analyzer System was designed to carry out Sentiment Analysis for Kenyan issues based on data obtained from Facebook and Twitter This is inline with our earlier stated objectives in section 1 3 above To ensure realization of the objectives the research designed the application with four main modules outlined below i
44. ata functionality allows the actor associated with the use case to search for and fetch desired data from Facebook and Twitter Preconditions The actor has an account created in the system The actor s profile has the Fetch Data function The actor has been authenticated and has access to the system The function is available via the user interface The actor has obtained an access token from Facebook and fed it into the Sentiment Analyzer System through the Parameters interface en Te o NO Triggers Actor selects Fetch Data function from the System administration menu Basic Course The following shall be the procedure to fetch data from social media in the system 1 The actor navigates to the Analyzer menu and selects a Fetch Data function 2 System displays interface UI9 to facilitate searching of posts from Facebook and Twitter 3 Actor enters search parameters as specified in UC 006 BR1 and clicks on the Search button 4 The system sends request for the data to Facebook and Twitter through the Graph API and Twitter API respectively 5 Facebook and Twitter servers respond to the request by returning data in JSON format that meets the search Keyword s entered 6 System decodes the JSON data returned into separate messages then displays them on the interface for the user to view The message attributes fetched are as specified in UC 006 BR2 41 T Actor views the messages f
45. ch User Form 3 Enter search criteria then click Search button to locate the user of interest 84 4 System will respond with a list of users meeting search criteria provided otherwise no results will be returned 5 Select the user record of interest by clicking on the Choose icon on the extreme left column of the record 6 System will respond with a display of the user details in read only mode 7 View the details and click cancel button to exit Mozilla Firefox Sees File Edit View History Bookmarks Tools Help a http localhost 8087 changePassword faces 8 FI locahost 8087 icloud sa changePassword Face e Pa SYSTEM ADMINISTRATION ANALYZER LOGOUT SEARCH USER Search Criteria Surname Other Names National Id Number User Name Search Reset List of Users Choose UserName Staff No Surname Othernames Email 2012 Edwin Wanyama UI7 Search User Form Fetch Data from Facebook and Twitter To fetch data from Facebook and Twitter perform the following steps 1 While logged in the system navigate to the ANALYZER menu and select the FETCH DATA submenu as shown in interface UIS Analyzer Menu and Menu Items 2 System responds by displaying UI9 Fetch Data Form shown 3 Enter keyword s to search for and far back in terms of days that the search should go to from the current date 85 4 Click on Search b
46. chedPosts size Twitter posts System out printin isaseead bk d rue ae dale String username google update get tweets as JSON Z3 null amp amp posts t Started on twitter URL url null try url new URL http search twitter com search json q keyWord catch MalformedURLException e TODO Auto generated catch block e printStackTrace ByteArrayOutputStream urlOutputStream new ByteArrayOutputStream try IOUtils getInstance copyStreams url openStream urlOutputStream catch IOException e TODO Auto generated catch block e printStackTrace String urlContents urlOutputStream toString System out printin urlContents substring urlContents indexoOf result s 9 System out printin urlContents parse JSON JSONArray jsonArray new JSONArray urlContents substring urlContents indexOf results 49 use for int i 0 i lt jsonArray length i JSONObject jsonObject jsonArray getJSONObject i System out printin jsonObject getString id System out printin jsonObject getString text System out printin jsonObject getString created_at FbPost fbp new FbPost fbp setPostId jsonObject getString id fbp setMessage jsonObject getString text fbp setCreatedTime new Bate jsonObject getString created_at fbp setClassified N fbp setPolarity null fbp setSource T fbp setSearchI
47. ct the FETCH DATA submenu as shown above in interface UI8 Analyzer Menu and Menu Items 2 System responds by displaying UI10 Classify Data Form Select messages that relate to a particular keyword by selecting the keyword using the dropdown field given on the interface 4 Click on View button to view the unclassified messages Click on Classify button to classify the messages 6 System informs you to wait as it performs the classification It then notifies you upon completion of the classification 7 Click on Cancel button to exit from the interface 87 t Sentiment Analyzer SYSTEM ADMINISTRATION ANALYZER LOGOUT CLASSIFY POSTS Criteria Search Phrase safaricom v View Data Done 2012 Edwin Wanyama UI10 Classify Data Form 88 View Results To view classification results do the following l While logged in the system navigate to the ANALYZER menu and select the VIEW RESULTS submenu as shown in interface UI8 Analyzer Menu and Menu Items System responds by displaying interface UI11 View Results Form Select Search Phrase for which you want to view classification results then click on View Data button System will display a tabulated summary of the classification results and also display the specific messages in colors that correspond to the three polarities positive negative and neutral You can therefore view the summary as well as go to the speci
48. d decision on whether to purchase the product If he she only reads a few reviews he she may get a biased view The large number of reviews also makes it hard for product manufacturers and service providers to keep track of customer opinions of their products For a product manufacturer there are additional difficulties because many merchant sites may sell its products and the manufacturer may almost always produce many kinds of products In this research we study the problem of classifying sentiments into positive negative or neutral and generating summaries and trends of the classified data These summaries are intended to find applicability in supporting customer care service centers in maintaining better customer relations To tackle this problem the study investigates issues that surround the development of a semantic analyzer of multilingual capability with the view of having it embedded in a web based application for use in rating opinions on products and services By applying sentiment analysis on product or service data obtained from Social Media Customer Care Centers are able to address issues before they go viral to the extend escalating into product recalls or bad publicity for a company 1 3 Objectives The purpose of this project is to 1 Develop techniques for harvesting and storing data from Social Media 2 Using the techniques developed harness and store data on various products and services in Kenya 3 Develop a
49. d and has access to the system 2 The actor profile has the function to Classify Data 3 The function is available via the user interface Triggers Actor selects the Classify function from the Analyzer menu Basic Course The following shall be the procedure to classify data in the system 1 The actor navigates to the Analyzer menu then selects Classify Data function 2 System displays interface UI10 to facilitate classification operation 3 User specifies messages to be classified by selecting search phrase related to them from a dropdown field then clicks on a View button 4 System displays unclassified messages for actor to view 5 Actor then clicks on Classify Data button 6 System calls the Python classifier module 7 The classifier program performs its activities as specified in UC 007 BR1 8 End of system use case Alternative Paths None AP Post Conditions 1 Polarity of each message is determined and stored for later display Exception Paths EP Includes UC 001 Priority High Frequency of use High Business Rules UC 007 BR1 1 Imports NLTK toolkit and other necessary tools 2 Reads in classifier training data from files where they are stored 3 Extracts training features from the data read in 4 Trains Naive Bayes classifier 9 Connects to the database where Facebook and Twitter data are 43 stored 6 Loops through each uncla
50. e aa aaea eat eesi Eae e Th 51 3 0 REsults and FInd oS aa E pas qe E tod t nce piu E tne eae 51 5 1 Test runs and Presentation of Results ona ined nee 51 5 1 1 Test runs with Na ve Bayes Classifier eese 51 3 1 2 Sentiment Analyzer System results esee 53 5 2 _ Eval ation Of Besulls hee to nis etse i toti Cos lite be oen saae 54 3 3 Discussion of Results een trei re eco t e te eeu ase e esa ecu lee head 54 CHAPTER Gi io utoquboniti tussi i equite tiem e uetus ddp i ied 56 Conclusion and Future Work esie dedidit n denda iae ee borde dii gass 56 CCOHCIUSIOT 54e ed toda eo C e Mec Leod DES LL SLE Lo pu 56 Lartinations Paced acis cede o e m 56 Future Work ect det dttaot ppt i tee otl code i Det cd dud pida ae 57 PRE CLC CCS MM NMPA TE 58 P Noel QR eee 60 Appendix 1 Sample Source ode see tour oet seede aate tod eutieutaediteud iban 60 Appendix 2 Installation Instructions eene 77 Appendix 3 User manual for sentiment Analyzer System sess 80 Appendix 4 Test Cases and Test Evidence 3c oet cit otere aborde ieas 91 vii List of Tables Table 1 Activities carried out to achieve objectives eene 23 Table 2 Effect of stop words on unbalanced corpus eee 51 Table 3 Effect of removing low information gain features with stop words retained 52 Table 4 Effect of stop words on balanced
51. ed details create an account for to see and rectify Enter invalid System System gives email should give The email address message The must be in the email must be format in the format yourname yo yourname y urdomain com ourdomain co message Click on System System exits Cancel should exit the user details button the user interface and details takes focus interface and back to the take focus main form back to the main form without saving details captured if any Click on System System saves Save button should save new user s after new user s details creates an account for the new user 92 details the new user then generates then generate and displays and display the login the login credentials for credentials for user the user iii Modify User Details Test Test case Test case Test steps case id name description Steps Expected Actual Outco me TC 3 Modify User To verify that Activate System System Test Case the Modify module by should give displays User Details clicking on Search User Search User functionality Modify User interface interface works correctly Details submenu Click on System System gives a Search should give a list of all users button list of all in the system without users in the entering any system search criteria Enter criteria Syst
52. ed other features beyond n grams such as POS tags and parse trees which were found to lack significant improvements They also studied multiple classification algorithms for processing large scale data In this regard three algorithms 1 Winnow ii a generative model based on language modeling and iii a discriminative classifier that employs projection based learning were explored Their experimental results showed that the discriminative model significantly outperformed the other two kinds of models Hu and Liu 2004 performed a research in which they mined and summarized customer reviews crawled from the web over a certain product They studied the problem of producing feature based summaries of customer reviews of products sold online Their task was performed in three steps that involved 1 identifying features of the product that customers have expressed their opinions on called product features 2 for each feature identifying review sentences that give positive or negative opinions and 3 producing a summary using the discovered information A number of novel techniques such as part of speech tagging frequent and infrequent features identification compactness pruning and redundancy pruning were used to achieve results Gitau and Miriti 2011 carried out sentiment analysis using unigrams emoticons and bigrams that were mined from Twitter on Kenyan issues They used the Naive Bayes model to perform polarity classification
53. em No results that does not should fetch displayed match details any results of any user existing in the system Choose a System System record from should display displays user s the displayed user sdetails details on list by on another another clicking on interface in interface in the icon under edit mode edit mode the Choose column Make changes System System to user record should confirms and click on confirm successful Save button successful save operation save operation for the changes made for the changes made on the user 93 on the user record record Make changes System System does to user record should notify notify the user by removing some details the user that the missing that the omitted details like email and details are are required click Save required button Click on System System exits Cancel should exit the interface button while the modify without saving in the midst of user details changes making interface changes without saving changes that have been done iv Change Password Test Test case Test case Test steps case id name description Steps Expected Actual Outcome TC 4 Change To verify that Activate System System displays Password Test the Change module by should display Change Case Password clicking on Change Password functionality Change
54. ersion The system will respond with a java version message if java is correctly installed b Tomcat Application Server Installation Instructions 1 Download windows installer for Tomcat and install it as a windows service Use the checkbox on the component page to set the service as auto startup so that Tomcat is automatically started when Windows starts T11 c d Python Tools Installation Instructions Navigate to folder named Python tools on installation CD You see a number of tools to be installed Install the tools in the following order by double clicking on each a Python 2 6 6 b numpy 1 5 1 win32 superpack python2 6 c matplotlib 1 0 1 win32 py2 6 d PYYAML 3 09 win32 py2 6 e nltk 2 0b9 win32 Go to programs and launch Python 2 6 6 IDLE Python GUI Go to file gt Open and browse to CD Select customdatapath creator py program file and run it to create a custom nltk data path Copy folder named corpora with all its contents from installation CD and paste it under the nltk data folder Check for it under C Documents and Settings lt user gt Copy file named fb_classifier py from installation CD and paste it under drive C i e C fb_classifier py Open Control Panel and go to Tools gt folder options Select File types tab then click on new button to set a new file association type In the Create new extension dialog that pops up enter py and specify the associated file type as Python MySQ
55. esseessersseessseeesseesseest 27 3 5 1 API customization for interfacing with Social Media 27 3 5 2 Pata OUSCHON 245 5 toe dee du 28 3 5 3 Data Preparation for Classifier training eee 29 3 5 4 Wal VG Bayesi Classifier ER ORDEN Gn a metes lens 29 3 5 5 Feature Extraction and Classifier Development esses 20 3 5 6 Evaluatiomor Class Mer sooo needed ecd epe aeo nidore se eoa oe ed 30 3 5 7 Implementation of the System iini ertt teo eaae esee an eeu ERE eue 31 CHAPTER Me S 32 4 0 Design and Implementation of Sentiment Analyzer System sess 32 4 1 System Design Speciicatons ox ieee eee eee LN ele eee 32 4 1 1 D ystem DOSSHDUGEH 3 oed ne f tw ess n d s term cd ipae Miei qe dade 32 4 2 PRC UOT S eeu oss o tuf EM eL ode pate 34 4 3 USE ECL p D TCU E PESE 34 4 2 Architectural Desten 3 5 e eu i e RU Diete i eiu e 46 4 2 1 Zxrehitecturd OVeEVIeWw udo beste Ot abut abre ost but ed Select cio unite 46 4 3 System Implementation ce pete er I MEA en PRISES EO Ue ceaesadvagaceysnseeconsvavaatesepones 47 4 3 1 Prone Bnd 24 2e tbi p dota undc nt 47 4 3 2 Application Logic Middle tier eese 48 4 3 3 Backend ce mmm 48 4 3 4 Classifier Module a5 ee hee 48 4 3 5 bata Model sedesie to n Ee bei Reece e eU utente 49 vi 44 Summary of Methodology tete etta sre o ees ee osea ee ee de eda sa seda 50 Sir Ped lsl RP th
56. etched and if satisfied clicks on the Save button to store them to the database 8 System stores the messages and notifies the actor 9 End of system use case Alternative Paths AP Post Conditions 1 Data of interest is stored in the database Exception Paths 1 If no data is found then nothing is returned to the system EP Actor adjust search parameters and repeats the search Includes UC 001 Priority High Frequency of use medium Business Rules UC 006 BR1 Search parameters 1 Keywords 2 Number of days how far back in terms of days from the current date UC 006 BR2 Message attributes 1 Post Id 2 Message 3 Created time 4 Source Facebook or Twitter Special None Requirements Assumptions None Process Owner Research Team Notes and Issues 42 4 1 3 7 Classify Data Use Case ID UC 007 Use Case Name Classify Data Version Version Created By Date Created Last Updated Date Last No By Updated 1 0 E Wanyama 6 Apr 2012 E Wanyama 6 Apr 2012 Actors User Sentiment Analyzer System and Python Classifier Description Classify Data functionality allows the actor associated with the use case to classify each of the messages in the data that was fetched and stored into three polarity categories negative positive and neutral Preconditions 1 The actor has been authenticate
57. fics Click on Generate Pie Chart button to view a presentation of the classification summary in a Pie chart System will display a Pie Chart with three polarities coded in three different colors Red color representing Negative polarity Blue color representing Neutral polarity and Green color representing Positive polarity Click on Generate Bar Chart button to view the trends of the classification summary over time in days Click on Done button to exit from the interface 89 gt Mew Data j Cerere Gens Dore Summary POLARITY NUMBER PERCENTAGE roomve xeseTE Em nevea 8 mes Toat wono Tent e as 40 a asi HT A i 220 LI asd x lt 10 x d osf xa T ss ia maen amoni mnao amean anon on Max Records Per Page fo terein Postid Mecaage Searoh fem ure 2 r EES as 13 UI11 View Results Form 90 Appendix 4 Test Cases and Test Evidence i Authenticate User Module Precondition Browser launched and application accessed by user through its URL Test Test case Test case Test steps caseid name description Steps Expected Actual Outco me TC 1 Aunthenticate To verify that the Enter invalid System System gives user Test Case Authenticate login Id and should give Invalid user valid Invalid Username functionality password and Username message works c
58. gatives 367 neutral sentiments in the file for neutrals and 559 positive sentiments in the file for positives During training of the classifier 7596 of the cleaned data combined is used as training data while 25 of the data is used as the testing set 3 5 4 Naive Bayes Classifier The Naive Bayes Classifier is based around the Bayes rule which is a way of looking at conditional probabilities that allows you to flip the condition around in a convenient way A conditional probability is a probability that event X will occur given the evidence Y That is normally written P X Y The Bayes rule allows us to determine this probability when all we have is the probability of the opposite result and of the two components individually P X Y P X P Y X P Y 3 5 5 Feature Extraction and Classifier Development The NLTK toolkit was used to realize a feature extractor and a Naive Bayes classifier The Naive bayes classifier requires that the training features be rendered in feature label pair for it to recognize that a particular feature belongs to a particular label In our case the file category negative positive or neutral from which a sentiment comes from is the label for that sentiment while the feature is the sentiment Since our sentiment classification is restricted to the sentence level the data had to be split into a file per sentiment per category We use scripts to achieve this splitting We then use an algorithm in
59. h likelihood of 59 1 of getting false negatives and false positives in the neutral class One possible explanation for the above results is that people use normally positive words in negative reviews but the words are preceded by a negating word such as not or some other negative word such as not great And since the classifier uses the bag of words model which assumes every word is independent it cannot learn that not great is a negative 55 CHAPTER 6 Conclusion and Future Work Conclusion In this research work we were able to build an application that has the ability to fetch and store data searched on various products and services from two most popular social media sites Facebook and Twitter A number of classification models were considered as specified in the literature review out of which we chose to use the Naive Bayes classifier model because of its simplicity in adapting it to the data collected We developed a Naive Bayes classifier that integrates an information gain heuristic using the Natural Language Tool Kit and trained it on a preprocessed dataset from Social Media The results obtained from experiments with the classifier see Chapter 5 above show that the classifier is capable of performing classification with an accuracy of 78 996 for sentiments obtained from Social Media This is near human accuracy as apparently people agree on sentiment only around 80 of the time Most of the sentiments in t
60. hartData public String getDay return day public void setDay String day 70 this day day public Integer getPositive return positive public void setPositive Integer positive this positive positive public Integer getNegative return negative public void setNegative Integer negative this negative negative public Integer getNeutral return neutral public void setNeutral Integer neutral this neutral neutral FbPost java package ics wanyama analyzer import ics common data PersistentDataEntity import ics common general Address import java util import javax faces context FacesContext public class FbPost extends PersistentDataEntity private int ig private String postId private String message private String classified private String polarity private Date createdTime private String searchItem private String source public FbPost public int getId return id public void setId int id this id id public String getPostId return postId public void setPostId String postId this postId postId public String getMessage return message 71 public void setMessage String message this message message public String getClassified return classifieg public void setClassified String classified this classified classified public String getPolarity return polarity public vo
61. hed a fervent and more lasting interest in mobile Internet activities a great many of them with a social element New options for Internet based social interactions continue to arise the recent launch of Google being one of them Analytics have changed over time to keep pace with the innovations in online community platforms Marketing analysis has evolved to take advantage of the constantly growing reach of social media Website statistics have expanded from simple analysis of Internet user behavior to contemporary analysis that includes social analytics By 2010 even URL shortening services such as bit ly were providing analytics to help individuals and businesses track link distribution Over the years people have demonstrated a strong desire to utilize the Internet to connect with others to be social and find others with whom to relate and share interests Businesses have studied these wishes and adapted to them to tap into social networking tendencies as a way to find and cultivate customers just as customers find and cultivate relationships amongst one another and also to get feedback from customers over services and goods offered Whatever the future of Internet based communication holds it is assured that social media elements will remain significant 2 1 3 Users of Social Media According to Openbook 2011 a research done in 2011 found out that Facebook Youtube and Twitter form the cream of the most popular top 10 social
62. his data are expressed partly in English Swahili Slang and Sheng thus formal language is scarcely used We therefore conclude that the model of classification selected is ideal for the kind of data collected from social media on Kenyan products and services Finally we integrate the techniques and methods developed into a web based application for use in providing Sentiment Analysis with respect to products and services in Kenya The application provides user friendly features and its architecture can also be used in viewing results of other classification approaches Limitations Faced A number of limitations were faced as listed 1 We were not able to fetch more information associated with sentiments fetched from social media due to restrictions that prevent running of queries that have joins 56 While fetching data the requests are limited to a time span of 30 seconds beyond which a fetch operation is timed out and disconnected This limits the amount of data that can be fetched on a search keyword The use of positive words preceded by negations such as not in negative sentiments leads to erroneous classifications since the classifier uses the bag of words model which assumes every word is independent It cannot therefore learn that not great is a negative Based on the data obtained from Kenyan opinions we observed that the language used is mostly slang Kenyan slang is constantly changing coupled with the idea of lack
63. id setPolarity String polarity this polarity polarity public Date getCreatedTime return createdTime public void setCreatedTime Date createdTime this createdTime createdTime public void setSearchItem String searchItem this searchItem searchItem public String getSearchItem return searchItem public void setSource String source this source source public String getSource return source FbUtility java package ics wanyama analyzer import import import import import import import import import import import import import import import import import import static java lang System currentTimeMillis static java lang System out ics common data PersistentDataEntity ics wanyama analyzer FbPost ava io ByteArrayOutputStream ava io IOException ava net MalformedURLException ava net URL ava util ArrayList ava util Calendar ava util Date ava util GregorianCalendar ava util List avax el ELContext I 1d duo cds Cols Co C do do Crise ds eG avax faces context FacesContext org jfree io IOUtils winterwell json JSONArray winterwell json JSONObject 72 import com restfb Connection import com restfb DefaultFacebookClient import com restfb FacebookClient import com restfb Parameter import com restfb types Post import com restfb types User public class FbUtility private final F
64. in different domains they can be significantly different Even in the same domain the same word may indicate different opinions in different contexts For example in the sentence The battery life is long long indicates a positive opinion on the battery life feature However in the sentence This camera takes a long time to focus long indicates a negative opinion Fourthly integration of the five pieces of information in the quintuple so as to get a match is a complex task That is an opinion must be given by an opinion holder on a certain feature of an object at a certain time To make matters worse a sentence may not explicitly mention some pieces of information but they are implied due to pronouns language conventions and the context To deal with these problems we need to apply NLP techniques such as parsing word sense disambiguation and coreference resolution in the opinion mining context Other 18 challenges include domain dependency irony and sarcasm and social elements Awareness of the multifaceted nature of opinions gives us a foundation upon which to conduct our search and analysis 2 5 Privacy Concerns In recent years social network research has been carried out using data collected from online interactions and from explicit relationship links in online social network platforms such as Facebook LinkedIn Flickr and Instant Messenger Bonchi et al 2011 The mining of this data makes it
65. ing Social networking allows an individual to create a profile for themselves on the service and share that profile with other users with similar interests to create a social network Users can choose to have public profiles which can be viewed by anyone or private profiles which can only be viewed by people that the users allow Users can usually post photographs music and videos on their site Some of the most popular of these sites include Facebook http www Facebook com and Twitter http www twitter com Social Photo and Video Sharing These sites are also known as Content Hosting Services Content hosting or content sharing sites allow users to upload content that they have created for others to view Two of the most popular of these sites are YouTube www youtube com for videos and Flickr www flickr com for photographs Users can also create an individual profile and list their favorite photos or videos Users are able to rate and comment on the videos or photos posted and provide feedback to the creator and other users Wikis This is a collaborative website that anyone within the community of users can contribute to or edit A wiki can be open to a global audience or can be restricted to a select network or community Wikis can cover a specific topic or subject area Wikis also make it easy to search or browse for information Although primarily text wikis can also include images sound recordings amp films Wikipedia http en wik
66. ipedia org the free internet encyclopedia is the most well known wiki 2 1 2 Growth of Social Media According to Corbo 2012 the 1990s ushered in a number of innovations that were exciting to Internet communities at the time The earliest versions of web pages and webcams emerged in 1991 as did the adoption of mp3 as a standard audio file Geocities surfaced in 1997 providing a platform for individuals and businesses to operate their own websites within Geocities virtual communities Also debuting in 1997 were AOL s Instant Messaging and Sixdegrees a social networking service that introduced the concepts of friends in social networking communities This era s advances would prove to be foundational to social media platforms and activities down the road The 2000s brought a tremendous growth in social networking platforms Friendster opened its virtual doors in 2002 followed quickly in 2003 by Myspace originally a Friendster clone 2003 also brought the world Skype and Linkedin representing the vast potential of VoIP and business networking respectively The opening of Facebook in year 2004 represented one of the most well known events in social media history It was the year the term social media was accepted into mainstream consciousness Twitter was born in 2006 and is credited with facilitating monumental social change across the world 2007 gave the world the iPhone which is also now etched into cultural consciousness It launc
67. is available via the user interface Bom Triggers Selection of Change Password function Basic Course The following shall be the procedure to reset the password of an existing user in the system 1 The actor navigates to the Change Password function and selects it 2 System provides interface UI6 for actor to capture old and new passwords 3 Actor provides the existing password and the new password 4 The actor confirms the new password with the system 5 System acknowledges successful change of password via password changed successfully message to the user 6 End of system use case Alternative Paths AP None Post Conditions 1 User password information updated in the system 2 Alog ofthe password change operation is stored Exception Paths EP 1 If password is invalid a System informs user to provide a valid password 40 Includes UC 001 Priority High Frequency of use medium Business Rules Special None Requirements Assumptions None Process Owner Research team Notes and Issues 4 1 3 6 Fetch Data Use Case ID UC 006 Use Case Name Fetch Data Version Version Created By Date Created Last Updated Date Last No By Updated 1 0 E Wanyama 1 Apr 2012 E Wanyama 1 Apr 2012 Actors User Sentiment Analyzer System Facebook and Twitter Description Fetch D
68. lear HibernateUtil getCurrentSession clear posts DataAccessService entityListFromHQL from FbPost f where f searchItem keyWord and f classified Y System out println KEy word keyWord System out println Number of posts posts size 64 setShowGraph false setPositive 0 setNegative 0 setNeutral 0 for PersistentDataEntity post posts if FbPost post getPolarity equalsIgnoreCase P positivett else if FbPost post getPolarity equalsIgnoreCase N negativett else if FbPost post getPolarity equalsIgnoreCase W neutraltt public void viewUnClassifiedData ActionEvent e keyWord String e getComponent getAttributes get key word posts clear posts DataAccessService entityListFromHQL from FbPost f where f searchItem keyWord and f classified N System out println KEy word keyWord System out println Number of posts posts size System Obt printlin e en Test posts Mr DataAccessService entityListFromHQL from FbPost E where f searchlItem WATER and f classified N size setShowGraph false setPositive 0 setNegative 0 setNeutral 0 for PersistentDataEntity post posts if FbPost post getPolarity equalsIgnoreCase P positivett else if FbPost post getPolarity equalsIgnoreCase N negativett else if FbP
69. nCollectionDataSource net sf jasperreports engine export JRXhtmlExporter org hibernate Hibernate org hibernate Query org jfree chart ChartFactory org jfree chart JFreeChart org jfree chart plot PlotOrientation org jfree data category CategoryDataset org jfree data general DefaultPieDataset org jfree data general PieDataset com restfb types Post test PieChart3DDemol class AnalyzerBean extends ParentBean FacesContext context FacesContext getCurrentInstance HttpServletRequest request HttpServletRequest context getExternalContext getRequest String remoteHost request getRemoteHost private FbPost currentPost new FbPost private List PersistentDataEntity posts ArrayList lt PersistentDataEntity gt private int positive 0 private int negative 0 private int neutral 0 private String accessToken private int days private String keyWord private boolean showGraph false private ArrayList lt SelectItem gt searchCriteria ArrayList SelectItem private int numberOfPosts 0 public AnalyzerBean Ws CI CI CJ UI public void setPosts List PersistentDataEntity posts this posts posts public List lt PersistentDataEntity gt getPosts return posts public void viewClasifiedData ActionEvent e new new keyWord String e getComponent getAttributes get key word posts c
70. networking sites used in Kenya Similar research available on Socialbakers 2012 indicates that Facebook penetration in Kenya in April 2012 was at 3 28 compared to the country s population and 32 87 in relation to number of Internet users The total number of FB users in Kenya had surpassed 1313400 having grown by more than 77600 users in a period of 6 months preceding April 2012 The user age distribution on Facebook in Kenya indicates that the largest age group is currently 18 24 with total of 512 226 users followed by the users in the age of 25 34 as shown in the Figure 1 Usage 13 15 16 17 18 24 25 34 35 44 45 54 55 64 65 0 Age Group Years Figure 1 User age Distribution on Facebook in Kenya Source Socialbakers 2012 User ratio Male Female Gender Figure 2 Male Female User Ratio on Facebook in Kenya Source Socialbakers 2012 The statistics also show that there are 64 male users and 36 female users in Kenya as at April 2012 shown in Figure 2 2 1 4 Setup of Social Media The current successes attained by social media would not have been possible without Web 2 0 technologies As mentioned earlier in section 2 1 Web 2 0 is a category of new Internet tools and technologies created around the idea that the people who consume media access the Internet and use the Web shouldn t passively absorb what s available rather they should be active contributors helping customize media and
71. ng e classified type string e polarity type string e createdTime type date e searchlItem type string e source type string class name ics wanyama analyzer Settings table fb settings id name code column CODE t property name value column VALUE type string class hibernate mapping 76 ype string Appendix 2 Installation Instructions a JDK Installation instructions 1 Search download and Install JDK 1 6 0 or a higher and compatible version from the internet 2 Go to programs gt Right click My Computer and select properties Then click on advanced tab menu 3 Under user variables section click on new button to setup a new user variable Provide the name as JAVA HOMPF and provide the variable value as C Program Files ava jdk1 6 0 i e the path to the bin folder where JDK was installed Note that the bin name should not be put at the end Click Ok to add the variable 4 Goto System variables section and select the Path variable and click the Edit button 5 Check under variable value to see if the path C Program FilesJavaydk1 6 0Nbin exists Add the path on the list incase it does not exist Remember to separate it from the previous paths by use of a semicolon 6 Click OK to save the new variable 7 Close the environment variables and system properties dialog 8 Test Java installation by using the commad below on windows command prompt java v
72. o create a customized development environment IDE from plug in components built by Eclipse members Eclipse is managed and directed by the Eclipse org Consortium The major advantage offered by Eclipse development platform is that it allows an IT department to mix and match development tools rather than being committed to a single vendor s suite of development products Though written in Java the Eclipse Platform supports plug ins that allow developers to develop and test code written in other languages Such capabilities have offered freedom and flexibilities that promote innovation for development of interfaces that afford better user experiences 11 2 2 Concept of Sentiment Analysis When conducting research or making every day decisions we often look for other people s opinions We consult political discussion forums when casting a political vote read consumer reports when buying appliances ask friends to recommend a restaurant for the evening And now Internet has made it possible to find out the opinions of millions of people on everything from latest gadgets to political philosophies Mejova 2009 The internet today contains huge quantities of data that keeps growing day by day A large number of websites have come up that provide people with an opportunity to express their opinion about certain things These include product review sites forums blogs and social network sites Consequently as a response to the growing availability
73. o identify tweets that were to be used to train the classifier Their tests were able to classify texts as positive negative or neutral However the only language they supported in their text was English Abbasi et al 2007 explored the use of sentiment analysis methodologies in the classification of web forum opinions written in multiple languages They evaluated the utility of stylistic and syntactic features in sentiment classification of English and Arabic content on movie review dataset U S and Middle Eastern web forum postings Specific feature extraction components were integrated to account for the linguistic characteristics of Arabic The Entropy Weighted Genetic Algorithm EWGA was also developed which is a hybridized genetic algorithm that incorporates the information gain heuristic for feature selection Their experiments indicated that stylistic features significantly enhanced performance across all test beds while EWGA also outperformed other feature selection methods indicating the utility of these features and techniques for document level classification of sentiments Cui et al 2006 identified two drawbacks faced by most of the current researches in Sentiment Analysis The first drawback concerns large scale real world data sets that current researches have to contend with Most previous work was conducted on relatively small experimental data sets employing at most a few thousand articles Building sentiment classifiers for
74. oduct reviews and blogs opinion holders are usually the authors of the posts Opinion holders are more important in news articles because they often explicitly state the person or organization that holds a particular opinion An opinion on a feature f or object 0 is a positive or negative view or appraisal on f or o from an opinion holder Positive and negative are called opinion orientations 2 2 3 Feature based Sentiment Analysis Model With the concepts in section 2 3 above in mind Liu 2010 defines a model of an object a model of an opinionated text and the mining objective which are collectively called the feature based sentiment analysis model Model of an object An object o is represented with a finite set of features F f1 f2 fn which includes the object itself as a special feature Each feature fi of F can be expressed with any one of a finite set of words or phrases Wi wil wi2 wim which are synonyms of the feature 16 Model of an opinionated document A opinionated document d contains opinions on a set of objects ol 02 or from a set of opinion holders hl h2 hp The opinions on each object oj are expressed on a subset Fj of features of oj 2 2 4 Types of opinions An opinion can be either one of the following two types Direct opinion A direct opinion is a quintuple oj fjk ooijkl hi tl where oj is an object fjk is a feature of the object oj ooijkl is the orientation
75. of the opinion on feature fjk of object oj hi is the opinion holder and tl is the time when the opinion is expressed by hi The opinion orientation ooijkl can be positive negative or neutral Comparative opinion A comparative opinion expresses a preference relation of two or more objects based on some of their shared features It is usually conveyed using the comparative or superlative form of an adjective or adverb e g Coke tastes better than Pepsi 2 3 Objective of Sentiment analysis Liu 2010 sums up his observations that given an opinionated document d the Objectives of sentiment analysis on direct opinions are 1 To discover all opinion quintuples oj fjk ooijkl hi tl in d and 2 To Identify all synonyms Wjk of each feature fjk in d Liu further observes that not all five pieces of information in the quintuple need to be discovered for every application because some of them may be known or not needed For example in the context of online forums the time when a post is submitted and the opinion holder are all known as the site typically displays such information A Sentiment can be identified at several levels which include the overall document e g product review blog forum post a sentence or a specific object attribute For each level search and analysis operates under somewhat different assumptions 17 2 4 Technical challenges A number of technical challenges have been observed in sentiment analy
76. of tweets into positive and negative The results achieved from their work suggest that the Naive Bayes Model of classification can be used as a starting point with relative ease to perform Sentiment Analysis with good results on Social Media They suggest further work to be done on additional Social Media players such as Facebook and Youtube 15 2 2 2 Elements used in Sentiment Analysis Liu 2010 identifies that five elements are used in sentiment analysis These include the object attribute opinion holder opinion orientation and strength and time An object refers to the target entity that has been commented on It may have a set of components or parts and a set of attributes or properties which are collectively called the features of the object We can use n example of a particular brand of cellular phone as an object It has a set of components e g battery and screen and also a set of attributes e g voice quality and size which are all called features An opinion can be expressed on any feature of the object and also on the object itself For example in I like iPhone It has a great touch screen the first sentence expresses a positive opinion on iPhone itself and the second sentence expresses a positive opinion on its touch screen feature Opinion holder The holder of an opinion is the person or organization that expresses the opinion An opinion holder is the person who expresses opinion about the object For pr
77. oid setCurrentPost FbPost currentPost this currentPost currentPost public FbPost getCurrentPost return currentPost public void saveFetchedPosts try fbp getMessage Successfully HibernateUtil beginTransaction for PersistentDataEntity fbp posts if FbPost fbp getMessage null amp amp FbPost length 200 HibernateUtil save FbPost fbp HibernateUtil commitTransaction this currentPost setSubmitMessage Posts saved catch Exception e e printStackTrace this currentPost setSubmitMessage An error occurred in trying to save the posts public void generateChartMethod OutputStream out Object data SYStem Out print 7 we ew oie eww a we generating chart System out printin cicacsuevis Positive t this getPositive System out println inm e ng Negative ud this getNegative 67 System out println i e emn Neutral x i this getNeutral PieChart3DDemol charti new PieChart3DDemol Summary PieDataset dataset createSampleDataset this getPositive this getNegative this getNeutral JFreeChart chart ChartFactory createPieChart Summary dataset true true true urls BufferedImage buffImg chart createBufferedImage 500 width 375 height BufferedImage TYPE INT RGB image type null try ImagelO write bufflmg jpeg out catch IOException e
78. ollect training data from Facebook in both English and Swahili ii Is the chosen method of classification ideal for the kind of data that has been collected li Once the data is collected is the classifier able to be trained iv Are we using the correct features for classification v Are the results of the tests on the data accurate in terms of quality or are the results unfavorably skewed towards one sentiment vi Is the Web application developed able to provide sentiment analysis on data of topical issues extracted from Facebook and Twitter In the evaluation of the classifier we used Natural Language Toolkit inbuilt metrics to measure accuracy precision and recall of the developed classifier 3 5 Execution of methodology 3 5 1 API customization for interfacing with Social Media Facebook and Twitter both provide APIs that can be used for searching and fetching post messages and tweets that have keywords specified in the search criteria With these tools one is able to fetch posts and tweets on topics of interest from the two social networking sites Search mechanisms and requirements for the two sites differ as explained below Facebook data access Facebook provides search capability through the Graph API The Graph API as such allows you to easily access all public information about an object You can search over all public objects in the social graph with https graph facebook com search We _ used _https graph facebook com sea
79. on MySQLdb connect host localhost user root passwd db test d ocreate a cursor cursor connection cursor d FPetch data i e post id and post message cursor execute SELECT post id post message FROM fb posts where classified N d get the resultset as a tuple result cursor fetchall d Tterate through each post message as you dR preprocess remove unwanted characters and stop words then classify it for record in result print record 1 postid record 0 message record 1 62 message message translate None S _ print message tokenized msg word tokenize message strip lower d Assign the results of the classification i e the label to a variable polarityrslt classifier classify word feats tokenized msg if polarityrslt pos polrslt P print polarityrslt elif polarityrslt neg polrslt N elif polarityrslt neu polrslt W sqlstmnt UPDATE fb posts SET polarity s WHERE post id s polrslt postid print sqlstmnt d Using the post id as identifier Update the database record with the classification result label cursor execute Update fb posts set polarity s classified s where post id s polrslt Y postid connection commit dx Trap any er
80. ord filtered word feats words return dict word True for word in words def word_feats words return dict word True for word in words E AE aE aE aE AE aE aE a aE aE aE aE aE aE aE a read in cleaned facebook and twitter data cleanreader CategorizedPlaintextCorpusReader C Documents and Settings edwin nltk_data corpora clean r pos neg neu txt cat pattern r pos neg neu wt txt cleannegids cleanreader fileids neg 1000 cleanposids cleanreader fileids pos 600 cleanneuids cleanreader fileids neu 350 word fd FreqDist label word fd ConditionalFreqDist 60 for word in cleanreader words categories pos word fd inc word lower label word fd pos inc word lower for word fd inc word lower word in cleanreader words categories neg label word fd neg inc word lower for word fd inc word lower label word fd label pos word count neg word count labe neu word count label total word count word scores word fd pos word fd neg word fd neu pos word count neg word count neu word count word in cleanreader words categories neu neu inc word lower N N N for word freq pos score freq neg score freq neu score freq in word fd iteritems abe BigramAssocMeasures chi sq l1 _word_fd pos word
81. orrectly click on message Submit button Enter valid System System gives login Id and should give Wrong User invalid Wrong User name or password and name or password click on password message Submit message button Enter invalid System System gives login Id and should give Wrong User invalid Wrong User name or password and name or password click on password message Submit message button Enter valid System System gives login Id and should give Wrong User blank Wrong User name or password and name or password click on password message Submit message button 91 ii Create user Precondition User is successfully logged in and has the function for accessible via the interface creating users Test Test case Test case Test steps case id name description Steps Expected Actual Outco me TC 2 Create User To verify that the Activate System System Test Case Create user module by should display displays form functionality clicking on form for for capturing works correctly Create User capturing new user details Save button details in red color for user submenu user s details Provide System System gives a incomplete should list of the user details providealist missing values and click the of the missing in red color for clear visibility capturing all requir
82. ory freearticles 612259 pdf Last accessed 7th Nov 2011 Yessenov K and Misailovic S 2009 Sentiment Analysis of Movie Review Comments Available http people csail mit edu kuat courses 6 863 report pdf Last accessed 13th Jul 2011 59 Appendix Appendix 1 Sample Source Code Classifier Source Code Htttit TASK SENTIMENT ANALYZER FOR FACEBOOK POSTS ttee FILE NAME fb classifier py tit AUTHOR EDWIN WANYAMA NGERO 4 ADMISSION NO P58 72924 2009 Train classifier using data in file Then use trained classifier to classify posts fetched from facebook d Import objects required import nltk import MySOLdb import collections itertools import nltk classify util nltk metrics import re import nltk classify util import nltk data fimport unicodedata from nltk classify import NaiveBayesClassifier ffrom nltk corpus import movie reviews from nltk corpus import stopwords from nltk corpus reader import CategorizedPlaintextCorpusReader from nltk probability import FreqDist ConditionalFreqDist from nltk metrics import BigramAssocMeasures dp Train the classifier using data stored in files then return message when classification is complete d Function for extracting words into a dictionary as it also removes english stopwords stopset set stopwords words english def stopw
83. ost post getPolarity equalsIgnoreCase W neutraltt public void showGraph ActionEvent e generateLineGraph setShowGraph true try FacesContext getCurrentInstance getExternalContext redirect icloud analyzer viewData faces catch IOException el TODO Auto generated catch block el printStackTrace public void fetchData ActionEvent e posts clear this currentPost setSubmitMessage String accessToken Settings DataAccessService 65 findFirst from Settings S where s code access token getValue keyWord String e getComponent getAttributes get key_word replace es Ng ty days Integer e getComponent getAttributes get days try FbUtility fbUtility new FbUtility accessToken keyWord days System oub printlm Vs i e xxu created fbUtility fbUtility runEverything System out dpbintlm Be oqszsoereras fbUtility runEverything successful posts addAll fbUtility getFetchedPosts catch Exception ex Syst m ouwt printin s secawewe meas fetch data failed ex printStackTrace public void classifyData ActionEvent e keyWord String e getComponent getAttributes get key word runPythonScript posts clear setPositive 0 setNegative 0 setNeutral 0 HibernateUtil getCurrentSession clear System out println
84. owth of Social Media ei Une tailed eed 7 2 1 3 Users oft SOCIAL Media 2 exo dete ee neen Bade dade e E 8 2 1 4 Setup of social M edia ee RT d iere 10 2 2 Concept of Sentiment ATialysts a iiosaeipoq eee teo eie etd ea PO oda ieee 12 2 2 1 Examples of Sentiment Research osi oom tst tb e e adopta sv 13 2 2 2 Elements used in Sentiment Analysis eene 16 2 2 3 Feature based Sentiment Analysis Model sees 16 2 2 4 Types of OPINIONS i sioe erret dese a a esa ena N RS a dea qa e enne ceanaenavandevges 17 2 3 Objective of Sentiment analysis ee tee ente reda soe PNE Yet as eo ii USt edt 17 24 hechnmalehallenses is asocio sts ATO Un one laco padded oue 18 2 Privacy Concerns Soo seda debe dani eat Cu e cee d E 19 26 Concept al Model eset Hof ee que fite qe ite 20 CHAPTER Dh onoodn unan n np DIM cibi udettv piat obe fip e tere i MS 22 SOTO COO BY op 22 ub SIRES CATE MS SIE A o eene E edema ed totque deut Rueda up Cu s 23 3 2 Data Collection Methods and Tools deoa etel eoe needs 23 3 2 1 Feature selection extraction esessseeseesresssssersessresressersresrersresreseresressesee 24 322 Bad ol SVO quest eee Een Re RR en 24 3 2 3 ACTASS UTC ALON eod ae eesi erti utes a eissir a a ostro cus 24 3 3 Implementation of the Sentiment Analysis Tool in a web based application 27 SE MU CIE E TN TUN 27 3 9 Execution of methodology essssesesssesssssesssesseessessseeesseersst
85. rch fields message amp q query amp type post amp 2T access token value The message part specifies that we only want to view the message attribute of the results query specifies the item we are searching for while type specifies that we are searching for posts in the social network The access token value is meant to fulfill the requirement for extended permissions that enable the search to read from the message stream We obtained an access token form the Graph API explorer of Facebook To obtain an access token one is required to sign in to Facebook navigate to the Graph API explorer app select the type of permission required then generate the token using the interface provided Twitter data access Twitter provides search capability through http search twitter com search json q searchfor The json bit tells twitter to return data in json format while the q parameter specifies the item we are searching for 3 5 2 Data Collection We used keywords for current topical issues as search criteria for collecting relevant data from Facebook and Twitter This exercise was repeated a number of times with the result being a dataset of over 3000 posts fetched so far from both Facebook and Twitter on various topical issues The data fetched from the two sites was then decoded from JSON JavaScript Object notation to text and stored in a MySQL database We only stored Facebook posts whose message does not exceed 200 characters Facebook
86. rch methods that were followed to achieve the overall objectives outlined in section 1 3 above We explain the sources of training data that were used data cleaning the way features were selected and categorized how classification was achieved and finally how the classifier will be evaluated The following activities were carried out in this research a b c d e f 2 Data sources and collection mechanisms Data cleaning and categorization into three categories i Positive ii Negative and iii Neutral Classifier development System Design System implementation Run some test and Evaluate the results Discuss results A summary of the activities carried out to achieve objectives specified in section 1 3 above are as tabulated No Objective How objective was achieved 1 Collect clean and categorize The Graph and Twitter APIs were customized and classifier training data from Facebook used to fetch data from Facebook and Twitter respectively The fetched data was stored in a database then eventually cleaned and stored in files labeled positive negative or neutral based on polarity of a tweet or message Develop a Sentiment classifier that is able to classify domain specific sentiments in posts as positive negative or neutral The study applied machine learning technique to develop and train a sentiment classifier based on the features extracted from data collected from
87. reas there is a large body of research on different problems and methods for social network mining there is still a gap between the techniques developed by the research community and their deployment in real world applications Bonchi et al 2011 Therefore the potential business impact of these techniques is still largely unexplored A constant flow of information is generated as customers interact with a company Harnessing the flow of information on social media and using it to bring the company ever closer to its customers is one of the applications that can benefit from sentiment analysis By mining opinions to understand the nature of customer s issues and then proactively solving them can lead to money savings and improved customer satisfaction Indeed Hu and Liu 2004 observe that in order to enhance customer satisfaction and shopping experience it has become a common practice for online merchants to enable their customers to review or to express opinions on the products that they have purchased With more and more common users becoming comfortable with the Web an increasing number of people are writing reviews As a result the number of reviews that a product receives grows rapidly Some popular products can get hundreds of reviews at some large merchant sites Furthermore many reviews are long and have only a few sentences containing opinions on the product This makes it hard for a potential customer to read them to make an informe
88. ros except MySOLdb OperationalError message errorMessage Error d n s message 0 message 1 connection rollback finally cursor close connection close AnalyzerBean java code package ics wanyama analyzer import java awt image BufferedImage import java io BufferedReader import java io IOException import java io InputStreamReader import java io OutputStream import java math BigDecimal import java math RoundingMode import java text SimpleDateFormat import java util ArrayList import java util Date import java util HashMap import java util Iterator import java util List import ics common UseMessageBundle import ics common converters DateConverter import ics common data DataAccessService import ics common data HibernateUtil import ics common data PersistentDataEntity import ics common ui ParentBean 63 import import import import import import import import import import import import import import import import import import import import import public javax faces context FacesContext javax faces event ActionEvent javax faces model SelectItem javax imageio ImageIO javax servlet http HttpServletRequest net sf jasperreports engine JRException net sf jasperreports engine JRExporterParameter net sf jasperreports engine JasperFillManager net sf jasperreports engine JasperPrint net sf jasperreports engine data JRBea
89. s for the data classification results in form of percentages and Charts 4 3 System Implementation The Sentiment Analyzer System was implemented as a three tier application using Java Enterprise Edition tools as given below 4 3 1 Front End Java Server Faces JSF was used in development of forms that constitute the user interfaces forms AJAX AJAX was used in input validations on user interfaces Richfaces we also used rich face components such as text Cascading Style Sheets CSS this was used in managing styles for the interfaces An example of CSS usage is in the coloring scheme used to display the three polarities We used red color for negative sentiments blue for neutral while green denotes positive sentiments 47 4 3 2 Application Logic Middle tier The business logic was implemented using Java programming Language This involved definition of entity properties into Plain Old Java Objects Pojos whereas the logic was done in Java Bean classes 4 3 3 Backend MySQL database was used to implement backend objects which include tables relationships constraints and sequences Hibernate was used to provide the requisite mapping between the database and the application logic 4 3 4 Classifier Module This module was implemented using Python programming language and the Natural Language Toolkit NLTK tools We customized the Naive Bayes Classifier that is available in the NLTK toolkit Part of NLTK data w
90. s in a review yet they offer poor accuracy because the writing in online reviews tends to be fragmented and less formal than writing in news or journal articles Many opinion sentences contain grammatical errors and unknown terms that do not exist in dictionaries Sentiment analysis or opinion mining is a discipline closely related to data mining that uses machine learning techniques and natural language processing to determine what a certain group of people feels about an issue Computers can use machine learning statistic and natural language processing techniques to perform automated sentiment analysis of digital texts on large collections of texts including web pages online news internet discussion groups online reviews web blogs and social media 1 2 Problem Statement Social media today contains huge quantities of textual data which are growing every day due to increased usage and entrants coupled with ease of generation of text and publishing What is hard nowadays is not availability of useful information but rather extracting it in the proper context from the vast quantities of content Yessenov and Misailovic 2009 This information can provide some value to marketers or researchers on the general perception of their products or services It is now beyond human power and time to seed through it manually therefore the research problem of automatic categorization and organizing data is apparent It has been observed that whe
91. s research is as shown in figure 3 i Training Feed the algorithm with Label and Feature pairs to build the classifier model Naive Bayes algorithm Training Feature Data Extractor ii Prediction Provide model with features to predict label x oe Naive Bayes ata xtractor Classifier Model Predicted Data Label Figure 4 Supervised classifier Model Naive Bayes assumes that all features in the feature vector are independent and applies Bayes rule on the sentence Naive Bayes calculates the prior probability frequency for each label in the training set Each label is given a likelihood estimate from the contributions of all features and the sentence is assigned the label with highest likelihood estimate The data corpus was divided into two groups training set and test set The training of the classifier was done on the sentences from the training set 25 The task of building the classifier was carried out using Python as a programming language Python s Natural Language Toolkit NLTK suite of libraries has rapidly emerged as one of the most efficient tools for Natural Language Processing According to semanticbible 2008 Python has the following desirable features that made it suitable for use in achieving objectives of this study i ii iii lv V It is a clean elegant object oriented language It has a rich set of library modules There is an active and readily avail
92. s wanyama analyzer import ics common data PersistentDataEntity import ics common general Address import java util import javax faces context FacesContext public class Settings extends PersistentDataEntity private String code private String value public Settings public String getCode return code public void setCode String code this code code public String getValue return value public void setValue String value this value value 75 analyzer hbm xml lt xml version 1 0 gt lt DOCTYPE hibernate mapping PUBLIC Hibernate Hibernate Mapping DTD 3 0 EN http hibernate sourceforge net hibernate mapping 3 0 dtd hibernate mapping class table fb_posts gt lazy false name ics wanyama analyzer FbPost id column ID name id type integer generator class increment xd property generated never nam co lum n POST ID property generated never nam co lum n POST MESSAGE property generated never nam col Lum n CLASSIFIED property generated never nam col Lum n POLARITY property generated never nam co lum n CREATED TIME property generated never nam co lum n SEARCH ITEM gt lt property generated never nam Gol class um n SOURCE e postId type string e message type stri
93. sage in a grid The messages are displayed in a color corresponding to the individual polarity for ease of identification and viewing 5 Actor clicks on Generate pie chart button to view a summary of the classification for the selected phrase 6 System displays a pie chart showing classification summary 7 End of system use case Alternative Paths AP None 44 Post Conditions User password information updated in the system 2 Alogofthe password change operation is stored 3 User audit information of the operation performed stored by the system Exception Paths None EP Includes UC 001 Priority High Frequency of use High Business Rules Special None Requirements Assumptions None Process Owner Research Team Notes and Issues 45 4 2 Architectural Design 4 2 1 Architectural Overview Figure 5 shows an overview of the system components and the interconnections between them that enable it to perform Sentiment analysis Sentiment Analyzer System overview Training amp Web Data Acquisition Data Store Feature extraction Classification Display a Search Facebook amp Data cleaning and eb Facebook dos for des t Labeling Categorize Y amp Twitter through APIs into pos neg amp neu Store messa Duplicate Checker post or wen Feature set Classifier training 3 extraction
94. sages in button messages in viewable form without read only specifying format for the keyword user to view Click on System System does 97 View Data should notify notify user to button user to specify specify a without a search search phrase selecting a phrase search phrase Click on System System does Cancel should exit exit from the button from the Classify data Classify data form form vii View Results Test Test case Test case Test steps case id name description Steps Expected Actual Outco me TC 7 View Results To verify that Activate System System Test Case the View module by should display displays UI11 Results clicking on UILI View View Results functionality View Results Form Form works as per the Results interface interface when design submenu the View which is Results under the submenu is Analyzer selected menu Click on View System System Data without should notify demands fora specifying user to specify search phrase search phrase keyword keyword Specify data System System to view by should display displays a selecting a tabulated tabulated search phrase summary of summary of related to it the the and click on classification classification View Data results for the results for the button specified specified search phrase search phrase It should also It also displays display all
95. se features are harmless but in aggregate we observed from the results given in parts ii of sections 4 1 1 1 and 4 1 1 2 above that low information features can decrease performance Thus eliminating low information features gave our model clarity by removing 54 noisy data This explains why the use of only the higher information features led to an increase in performance while also decreasing the size of the model We also interpret the following from an accuracy of 78 9 Positive precision of 68 2 Positive recall of 73 696 Negative precision of 85 196 Negative recall of 95 596 Neutral Precision of 76 6 and Neutral recall of 40 9 1 A sentiment given a positive classification is only 68 2 likely to be correct This precision leads to 31 8 false positives for the positive label 73 6 positive recall means that there is a likelihood of 26 4 of getting false negatives and false neutrals in the positive class A sentiment identified as negative is 85 1 likely to be correct This means a 14 9 likelihood of having false positives and neutrals in the negative class A negative recall of 95 5 means that many sentiments that are negative are correctly classified This implies very few false negatives for the negative category A sentiment given a neutral classification is only 76 6 likely to be correct This precision leads to 23 4 false neutrals for the neutral label A neutral recall of 40 9 means that there is a hig
96. ser details is provided beside each record in the Choose column 5 Actor selects a user s record to view by clicking on the link in the Choose column 6 System drills down to selected user details by displaying them in read only mode 7 End of system use case Alternative Paths AP Post Conditions 1 Logofthe user viewing operation is stored 2 User audit information of the operation performed stored by the system Exception Paths 1 Ifthere are no users defined within the system EP 8 System informs the user about the unavailability of users in the system Includes UC 001 Priority Medium 39 Frequency of use Medium Business Rules Special None Requirements Assumptions None Process Owner Research Team Notes and Issues None 4 1 3 5 Change Password Use Case ID SUC 005 Use Case Name Change Password Version Version Created By Date Created Last Updated Date Last No By Updated 1 0 E Wanyama 1 Apr 2012 E Wanyama 1 Apr 2012 Actors All authorized users Description Change Password functionality allows the actor associated with the use case to change password in the system Preconditions The actor has an account created in the system The actor s profile has the function to change password The actor has been authenticated and has access to the system The function
97. sis Liu 2010 The first is Object identification A blog may have sentiments expressed on different objects In such a case the problem lies in identifying the object on which a sentiment has been expressed without which the opinion is of little use In a typical opinion mining application the user wants to find opinions on some competing objects e g products The system thus needs to separate relevant objects and irrelevant objects The second is feature extraction and synonym grouping Current research mainly finds nouns and noun phrases Although the recall may be good the precision can be low Furthermore verb features are common as well but harder to identify To produce a good summary there is need to group synonym features as people often use different words or phrases to describe the same feature e g voice and sound refer to the same feature This problem is also very hard A great deal of research is still needed The third challenge is on opinion orientation classification The task here is determination of whether there is opinion on a feature in a sentence and if so whether it is positive or negative Existing approaches are based on supervised and unsupervised methods One of the key issues is to identify opinion words and phrases e g good bad poor great which are instrumental to sentiment analysis The problem is that there are seemingly an unlimited number of expressions that people use to express opinions and
98. ssified message post stored as it classifies it and updates its polarity field Special None Requirements Assumptions None Process Owner Research Team Notes and Issues 4 1 3 8 View Classification Results Use Case ID UC 008 Use Case Name View Results Version Version Created By Date Created Last Updated Date Last No By Updated 1 0 E Wanyama 6 Apr 2012 E Wanyama 6 Apr 2012 Actors User Sentiment Analyzer System Description View Results functionality allows the actor associated with the use case to view classification results in the system Preconditions 1 The actor s profile has the function to View Results 2 The actor has been authenticated and has access to the system 3 The function is available via the user interface Triggers Actor selects View results function from the Analyzer menu Basic Course The following shall be the procedure to View classification results in the system 1 The actor navigates to the Analyzer menu then select View Results function 2 System displays interface UI11 to facilitate viewing of results 3 The actor selects a search phrase keyword s then clicks on the View data button 4 System displays a summary of classification results in a tabular format where the polarity is displayed in total percentages of each polarity category System also displays each sentiment mes
99. t set def stopword filtered word feats words return dict word True for word in words if word not in stopset FE HE HE HEH ERE EE ERE ERE HER HERE ERE print evaluating best word features when using only the best 1950 words for i feats label in enumerate testfeats refsets label add i observed classifier classify feats testsets observed add i print pos precision nltk metrics precision refsets pos testsets pos print pos recall nltk metrics recall refsets pos testsets pos print pos F measure nltk metrics f measure refsets pos testsets pos print neg precision nltk metrics precision refsets neg testsets neg print neg recall nltk metrics recall refsets neg testsets neg print neg F measure nltk metrics f measure refsets neg testsets neg print neu precision nltk metrics precision refsets neu testsets neu print neu recall nltk metrics recall refsets neu testsets neu print neu F measure nltk metrics f measure refsets neu testsets neu print accuracy nltk classify util accuracy classifier testfeats classifier show most informative features print End of evaluating best word features d Establish database connection parameters from nltk tokenize import word tokenize try d oreate a connection object connecti
100. te Last No Created By Updated 1 0 E Wanyama 1 Apr 2012 E Wanyama 1 Apr 2012 Actors User and Sentiment Analyzer System Description Authenticate User functionality allows the user associated with the use case to authenticate their user account to access system functionalities Preconditions 1 The account of the user has been created within the system 2 The user has the right to access the system Triggers The actor launches the application by launching a browser and entering the address of the application Basic Course The following shall be the procedure to authenticate their account in the system 1 The actor launches the application by launching a browser and entering the address of the application 2 System responds by availing login interface UI1 3 The user provides the user name and password for system authentication 4 System validates credentials according to UC 001 BR1 and acknowledges the user by displaying interface UI2 This indicates successful authentication to the system else EP1 EP2 EP3 or EP4 apply 5 AP applies if it is the first login attempt and the password has not been changed 34 End of system use case Alternative Paths AP System displays interface UI6 for the actor to change password Post Conditions A log of the user authentication operation is stored User audit information of the operation performed stored by the system
101. tem keyWord System out println Twitter post complete jsonObject getString id if fbp getMessage null fetchedPosts add fbp void selection out println Selecting specific fields User user facebookClient fetchObject me User class Parameter with fields id name out println User name user getName void parameters out println Parameter support Date periodStart addDaysFromDate new Date 0 days Connection lt Post gt filteredFeed facebookClient fetchConnection me feed Post class Parameter with limit 300 Parameter with since periodStart 74 out printlin i s es SInce periodStart Ot printlm s5okassewewu e Filtered feed filteredFeed getData size void rawJsonResponse out println Raw JSON out println User object JSON facebookClient fetchObject me this fetchedPosts fetchedPosts public List lt FbPost gt getFetchedPosts return fetchedPosts count T String class public void setFetchedPosts List FbPost fetchedPosts public static Date addDaysFromDate Date enteredDate int noOfDays GregorianCalendar gc GregorianCalendar enteredDate getYear new enteredDate getMonth enteredDate getdate gc add Calendar DATE noOfDays gc add Calendar YEAR 1900 return gc getTime Settings java package ic
102. the classifier program 29 that returns a dictionary of feature sets that are rendered as feature label pair as explained above The returned dictionary of feature sets enables the classifier to learn to associate a feature with a particular label that is positive negative or neutral The algorithm is also structured to discard stop words from the dictionary of feature sets used for training We used the Naive Bayes classifier that ships with the Natural Language toolkit and customized it to suit our research needs Its customization is currently complete and functional It does the following l Makes the necessary imports of resources such as the categorized corpus reader that we use to read training data stored in files and a set of stop words 2 Reads in training data from files 3 Obtains the categories of labels to be used 4 Generates a feature set for training the classifier using a bag of words model 5 Splits the feature set into a training set and testing set 6 Trains a Naive Bayes classifier imported from NLTK toolkit using the training set 7 Tests the classifier accuracy using the testing set 8 Prints out the accuracy and informative features 9 Retrains the classifier with the entire combined Training and testing set set 10 Connects to the database and loops through each message or tweet as it classifies and updates the polarity of each record The classifier has been tested and found to be working
103. the properties of the original text that are relevant for the sentiment analysis task Unfortunately the exact algorithm for finding best features does not exist We therefore relied on intuition domain knowledge and experimentation for choosing a good set of features 3 2 0 Bag of words We used a simple bag of words model to perform feature extraction Usually opinion detection is based on the examination of adjectives in sentences though this can be misleading in cases that include sarcastic sentiments and negations As a result we considered polarities based on sentences more than they appear based on adjectives 3 2 3 Classification This research used a classification algorithm to predict the label for a given input sentence There are two main approaches for classification supervised and unsupervised In supervised classification the classifier is trained on labeled examples that are similar to the test examples whereas unsupervised learning techniques assign labels based only on internal differences distances between the data points In this classification approach each sentence is considered independent from other sentences Yessenov and Misailovic 2009 The label we were interested in this project is the polarity of the sentence We built a classifier based on the Naive Bayes method which is a supervised classification technique and trained it to 24 perform classification A model of the supervised classifier used in thi
104. trics except negative precision which went up by 0 8 We further see from the results given in parts ii of sections 4 1 1 1 and 4 1 1 2 above that removal of low information features improves the performance of the classifier on all the three metrics i e accuracy precision and recall for all the corpus sizes used in experimentation We also tested the effect of using a balanced corpus for training where we provided an equal number of words for positive negative and neutral words A general drop in performance measures was observed which improved with the increase in the corpus size We observe that on average the best classifier model is achieved when we used a modest corpus of 1950 most informative words which gave highest classifier accuracy of 78 9 Other metrics also appear to perform at an acceptable level when all results are considered 5 3 Discussion of Results The evaluation results in this study suggest that feature reduction steps such as removal of stop words should be taken very carefully since their removal could have a negative impact on classifier model as observed in this study Some stop words are highly discriminative features We deduce that out of the hundreds of features our classification model has some of them had low information These are features that are common across negative positive and neutral classes and therefore contribute little information to the classification process Individually the
105. umber of records displayed per records displayed per page and click displayed per page as on the refresh page to the required button new number specified if the records avallable are equal to or more than the 96 number specified Click on System System Save button should confirms once data has confirm successful been fetched successful save operation and save operation for the data considered to for the data fetched be satisfactory fetched vi Classify Data Test Test case Test case Test steps caseid name description Steps Expected Actual Outco me TC 6 Classify Data To verify that Activate System System Test Case the Classify module by should display displays Data clicking on Classify Data Classify Data functionality Classify Form Form works as per the Data interface interface design submenu which is under the Analyzer menu Choose System System keyword searc should inform classifies data h phrase from user to wait as and notifies drop down it classifies user upon field and click data related to completion as on Classify the selected expected Data button keyword then notify user of successful completion of process Select a System System does Search phrase should display display and click on a list of unclassified View Data unclassified mes
106. unctionality allows the actor associated with the use case to add a new user to the system Preconditions 1 The actor has been authenticated and has access to the system 2 The Create User function is available via the user interface Triggers The User selects function Create User from the System Administration Menu Basic Course The following shall be the procedure to add a new user in the system 1 The User selects function Create User from the System Administration Menu on interface UI3 2 System displays UI4 for capture of user details as specified in UC 002 BR1 3 Actor saves the added user 4 System validates entered details against rules specified in UC 002 BR2 Upon successful validation it assigns a password and user id to the added users account and displays the password and user id to the actor 5 Endof system use case Alternative Paths AP None Post Conditions Details of the new user stored in the database Log of the user creation operation updated 3 User audit information of the operation performed stored by the system N Exception Paths EP 1 If the actor has entered invalid data 8 User prompted to enter correct data 2 lf actor has not captured all required details 8 User prompted to capture all the required details 3 If the same user already exists within the system a User informed about the existence of the same user by a
107. ur comments or letting you vote on an article or it can be as complex as the process of recommending movies to a user based on the ratings of other people with similar interests Regular media is synonymous to a one way street where you can read a newspaper or listen to a report on television but you have very limited ability to give your thoughts on the matter Social media on the other hand is a two way street that gives you the ability to communicate too Web 2 0 is a category of new Internet tools and technologies created around the idea that the people who consume media access the Internet and use the Web should not passively absorb what is available rather they should be active contributors helping customize media and technology for their own purposes as well as those of their communities These new tools include but are by no means limited to blogs social networking applications RSS social networking tools and Wikis 2 1 4 Examples of Social Media Websites These include but are not limited to the following Social Bookmarking These include Delicious http del icio us and Blinklist http www blinklist com Users interact by tagging websites and searching through websites bookmarked by other people Social News Sites under this category allow a user to interact by voting for articles and commenting on them Examples under this category include http reddit com and http www digg com Social Network
108. using APIs that are readily available and free for use in accessing data from the two social networks It is a fact that the style and nature of writing posts on Facebook and Twitter is not strictly formal The data collected therefore had to go through some cleaning exercise before it was used in training the classifiers We therefore had to remove irrelevant characters such as URL links and repeated characters This was meant to ensure that the classifiers are trained on appropriate data for better performance 23 3 2 1 Feature selection extraction In order to perform machine learning it is necessary to extract clues from the text that may lead to correct classification Yessenov and Misailovic 2009 Clues about the original data are usually stored in the form of a feature vector F fl f2 fn Each coordinate of a feature vector represents one clue also called a feature fi of the original text The value of the coordinate may be a binary value indicating the presence or absence of the feature an integer or decimal value which may further express the intensity of the feature in the original text In most machine learning approaches features in a vector are considered statistically independent from each other The selection of features strongly influences the subsequent learning The goal of selecting good features is to capture the desired properties of the original text in the numerical form The study endeavored to select
109. utton to fetch the data 5 System will respond with a display of the fetched results if any data containing the keyword s is found 6 View data displayed by paging through with the help of the scrolling tool that is beneath the records You can also change the number of records displayed by changing the Max records field then refresh to view more or less 7 Click on Save button if satisfied with the fetched results to persist the data File Edit View History Bookmarks Tools Help A http localhost 8087 jtemplatesjlogin faces FR localhost s087 icloudtemplates login Faces r ec u PRA Sentiment Analyzer All y SYSTEM ADMINISTRATION ANALYZER LOGOUT FETCH DATA CLASSIFY DATA VIEW DATA 2012 Edwin Wanyama UI8 Analyzer Menu and Menu Items 86 3 Mozilla Firefox Dek File Edit View History Bookmarks Tools Help j http localhost 80877 es successLogin faces 9 FR locathost 2087 icloud templates successLogin Face e P SYSTEM ADMINISTRATION ANALYZER LOGOUT FETCH POSTS Criteria Key Word Number of Days 1 v Search Save Results Max Records Per Page 5 Refresh Post Id Message Created Time Source 2012 Edwin Wanyama UI Fetch Data Form Classify Data To classify Data do the following steps 1 While logged in the system navigate to the ANALYZER menu and sele
110. vailable at http ai arizona edu intranet papers ahmedabbasi_sentimenttois pdf Last accessed 9th Nov 2011 Bird S Klein E and Loper E 2009 Natural Language Processing with Python st ed CA O Reilly Media Bonchi F Castillo C Gionis A and Jaimes A 2011 Social network analysis and mining for business applications Available http doi acm org 10 1145 1961189 1961194 Last accessed 10th Oct 2011 Boisen S 2008 Natural Language Processing in Python using NLTK Available at http semanticbible com other talks 2008 nltk main html Last accessed 9th Aug 2011 Corbo R 2012 The History of Social Media Evolution on the Internet Available http www technize com the history of social media evolution on the internet Last accessed 18th Apr 2011 Cui H Mittal V and Datar M 2006 Comparative experiments on sentiment classification for online product reviews In Proceedings of AAAI Facebook Team 2012 Facebook Platform Policies Available at http developers facebook com policy Last accessed 11th Jan 2012 Gitau E and Miriti E 2011 An approach for Using Twitter to perform Sentiment Analysis in Kenya University of Nairobi Kenya Hu M and Liu B 2004 Mining and summarizing customer reviews In Proceedings of KDD 04 the ACM SIGKDD international conference on Knowledge discovery and data mining 168 177 Seattle US ACM Press Liu B 2010 Sentiment Analysis A multi f
111. via the user interface aPwon gt 37 Triggers The actor selects function Update user details from the System Administration Menu Basic Course The following shall be the procedure to edit a user in the system 1 The user selects Modify user details from the System Administration Menu on interface UI3 2 System displays interface UI7 To facilitate location of user record 3 User enters search parameter for all or any of the attributes displayed and clicks on search button 4 System displays list of users matching the search parameters entered Actor selects user record that needs to be updated from the list 6 System displays user details on interface UI5 in edit mode T Actor makes changes as required and clicks on Save button to persist the changes 8 System confirms successful save operation for the changes made on the user record 9 End of system use case e Alternative Paths AP Post Conditions 1 Changes to the user stored in the database Exception Paths 1 Ifthe actor has entered invalid data EP 8 User prompted to enter correct data Includes UC 001 UC 004 Priority Medium Frequency of use Medium Business Rules UC 003 BR1 allowed operations 1 Actor is allowed to edit all attributes of the user except the National ID Special None Requirements Assumptions None Process Owner Research Team Notes
112. y lives In the recent past people were restricted to use of emails and phones in communication as faster and convenient means of communication These means are still in use and convenient but lack certain capabilities that are currently the selling features of social networks For instance with emails and phones one would be restricted to communicating to only those people whose contacts are known and with minimal or no information relating to friends of the party being communicated to Social network sites provide space and mechanisms of making new friends and sharing information easily Due to the success of online social networking and media sharing sites and the consequent availability of a wealth of social data social network analysis has gained significant attention in recent years In spite of the growing interest however there is little understanding of the potential business applications of mining social networks Bonchi et al 2011 Despite being rich in content social media unlike traditional media have unorganized content contributed by users often in fragmented and sparse fashion Users wishing to get useful information usually have to spend a lot of their time filtering useless information and yet are not able to capture the essence Though significant research efforts have been put in sentiment classification and analysis most of the existing techniques rely on natural language processing tools to parse and analyze sentence

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