Quick Introduction - T-Lab
Contents
1. F Quick Start Guide Help 22 PRIMARY DOCUMENTS TRLIAAENM T LAB Plus 2019 4 1 0 3 CORPUS BUSH SEPTT11 ANALYSIS PATHS MENU J CO OQCCURRENCE ANALY 515 En Word Associations Co Word Analysis and Concept Mapping Comparison between Word Pairs Sequences and Network A nahyrals Concordance a USER PROFILE O Expert KEY TERMS LIST N 281 A Automatic 5 Customized 5P Check I Change interface Fili EMGLISH Feshi oeae ursi T LAB Plus 2019 Quick Introduction Pag 6 of 29 5 Check the results C HELP O TERRORISM LJUHDERSTAHD CI FREEDOM O TIME I STRONG L1Tunay CO GOVERNMENT CONGRESS CO THANK Osoop TUHITED STA 1 JUSTICE rr E 1 terit a el _ ec e cas erso o 5 7 HE PRR mH Md a E 1 3 itis 8 z sels 220 Ss 8 8 iE B5 S 5 8 i i T 5 amp E T als T 6 Sa S E T LAB Plus 2019 Quick Introduction Pag 7 of 29 wwwtl ab it info tla b it www tlab it 6 Use the contextual help function to interpret the various graphs and tables Ed E T LAB Help Ko o 5 Mascondi Stamps 1 Word Associations Do occumence Analysis amp Word Associations Cod Pals n This T LAB tool allow2. aa B SEIT TERM 5 16 2 EXTRACT KEY CONTEXTS SAVE CONTEXTS AVAILABLE ITEMS TEXTS i ITEM occ CO FREEDOM CO TERRORISM UNDERSTAND O TIME O STRONG THANK O NEED O GOVERNMENT CLEAN OUT YOUR LIST OUT YOUR LIST T LAB Plus 2019 Quick Introduction Pag 25 of 29 B www tlab it ADD CUT WORDS FROM YOUR LIST DOUBLE CLICK 77 F b os T EXTRACT KEY CONTEXTS AVAILABLE 4 4152 LIST OF ASSOCIATED LEMMAS DUTPUT TABLES ITEM OCC American SPEECH 9 E I MINORITY i Arab Cosine 546 3 MISSION 21 campaign as_a matter of fact islam preaches peace The Mu C MISTAKE 14 Christian as I salute the flag m MOM combed dnas Ou ET KEY CONTEXTS SORTED BY WEIGHED DESCENDING ORDER E CO MORNING SPEEC H SEPI9 ler of 1 Move Cosine 546 L3 1 O MURDER ith Iz MUSLIM as_a_matter_of_fact Islam preaches peace The Muslim faith is a peaceful faith And pm 1 there are millions of good Americans who practice the Muslim faith who love their T 6 NATIONAL country as much as love the country who salute the flag as strongly as I salute the p C NECESSARY flag CO NEED when yx NEIGHBOR etr SPEECH SEPIO faith abd Cosine 511 C NIGHT 7 5 M to we NU
3. T LAB Plus 2019 Quick Introduction oon A l X i bs TRA aes 3 1 5 A a 3 a E TT OST a Rit SOURS d M 0 I Sisi RC E 4 N 9 S RRL DH 15 D ANA OS eS SAT SISSON z when So XT TOSE LMA WWD NEM Vp ore Van RCRA E HS VY VERRE je p LEY IE Ia TTE TIT 2 HCA ES RA SOD 2710453 sd UE TR S VESTE uv SOL ARTE ARSE West CEOCONIDIZCGHTSZIUCHERAWSEUTe Hum ACTS xxn AM EDU ESTER SEI PCR EAI DUS 2 IC A DA AE RTE AA REGIA pC STAR SN MES 2 2 ME ETAL INS qe y Rare SM ASSES Bata Gal EE a T 2 ECRIRE Ag DC LOG LL Catv WANE S 5 6 ARI 8 Spee Im r n MDW ie Se RAS FU vm bc 55 icd p uha uo 919 LD fs Val Sac iae ctm OMM Rag M EX Tree Sor yaw S e Mr Ven Kash f uL eee 4 eof icon E ufus P vile BRR foa conn 7e mf ww rip warmer ban We cut ffi xs 63 Cite cft 19 FC
4. WIRE T e HAE inen DIVISION SENSOR WORLD 2 CC L ON cone wes d X o 1 eee 22 SEMICONDUCTOR FIBER i MARKETER DIARY j LEADER p SALE DEVICE ai usas car DISTRIBI DISTRIBUTOR FREQUENCY CABLE TEST Euch PI in 0 ODY AMERICAN X Axis 2 GOVERNMENT LABEL REVENUE LE D mel SCIENCE T f VENTURE TREATMENT HEALTH O COMMERCIAL O CONTROL O DEVELOPMENT O DEVICE DIGITAL Oost 80111 amp BDH ED CD CD de LJ C d LJ LJ LED gno DEVELOPMENT Sequence and Network Analysis This T LAB tool which takes into account the positions of the various lexical units relative to each other allows us to represent and explore any text as a network That means that the user is allowed to check the relationships between the nodes 1 e the key terms of the network at different levels a in one to one connections b in the ego networks c within the community to which they belong d within the entire text network ONE TO ONE EGO NETWORK CHILD su mai ag T AS TLUM SEEKERS tnt ES j ASYLUM o COUNTRY vy A amma e EUROPE y g 9 FAMILY ew m P E FUGEE i EK XY pu amannenn t T gt 1 m GRANT S ar 777 3 a mt lt a a 1 a eo HELP SYRIAM 7 0 nil ai 1 m f HOUS
5. Y Fact 2 10 179 SELECT A VARIABLE VARIABLE lt PRES gt jens X Fact 1 Y Fact 2 Z gt Fact MO T LAB Plus 2019 Quick Introduction Pag 19 of 29 www tlab it info tlab it Cluster Analysis which requires a previous Correspondence Analysis and can be carried out using various techniques allows us to detect and explore groups of analysis units which have two complementary features high internal within cluster homogeneity and high external between cluster heterogeneity T L 8 CLUSTERS amp VARIABLES X Fac 1 10 209 amp Y Fact 2 8 8595 Copyright amp 2001 2011 Version 7 3 0 TABLES CLUST 13 CLUST 1 CLUSTERS VARIABLES SPEECH T CLUST 14 INTELLIGENCE ENFORCEMENT BATTLE HOLD REPORT TALK MONEY ADMINISTRATION DIFFERENT GLOBAL MORNING ACTION SHARE OPERATION THREAT ORGANIZATION PRESIDENT TELL FUND ACTIVITY FRONT REQUIRE FINANCIAL ASSET ORDER CENTER MINISTER PRIME PAKISTAN CUT OFF T LAB Plus 2019 Quick Introduction Pag 20 of 29 www tlab it C TOOLS FOR THEMATIC ANALYSIS These tools enable us to discover examine and map themes emerging from texts As theme is a polysemous word when using software tools for thematic analysis we have to refer to operational definitions More precisely in these T LAB tools theme is a label used to indicate four different entities l a thematic
6. www tlab it Viewing Style Border Style Font Size Plotting Method Data Shadows Grid Options Point Label Orientation Undo Zoom Maximize Customization Dialog Export Dialog 3 through the Modelling of Emerging Themes tool see below the mixture components described through their characteristic vocabulary can be used for building a coding scheme for qualitative analysis and or for the automatic classification of the context units 1 e documents or elementary contexts Fh a F 1 4 CLICK ON ITEMS TO ELIMINATE THEME lt FAMILY gt TYPICAL WORDS THEME lt FAMILY gt WORD PERCENTAGE SHARED WORDS HIGHT PROBABILITY IN 0 100 2 a FAMILY 61 42 PEOPLE 15 10 T LAB Plus 2019 Quick Introduction Pag 24 of 29 www tlab it MOS SAMMON S METHOD STRESS 0 1135 0 04 0 04 0 04 0 04 0 04 0 04 FI nona mara aram Hn ma gem Jj 4 the Key Contexts of Thematic Words tool see below can be used for two different purposes a to extract lists of meaningful context units 1 e elementary contexts which allow us to deepen the thematic value of specific key words b to extract context units which are the most similar to sample texts chosen by the user SELECT CONTEXT CORPUS SUBSET INPUT TYPE KEY TERMS TEXTS NEWTEXT CLEAN QUT YOUR BOX
7. de eu fup 1B jem eA Dfi whit Any mk sm l TE s 1 gt c yt 0 4 h wed v 5 Lene lan vule 5 sew otal PTS OLET WE ehe yk A D vnit fs eo vy CAT x qd Yaw 7 2 poen au PL eM eV AREY bye i fle an Pho psu cla 23 rth PA pu Jing RT tert y hie i Lfecoi Ao Art YA ani 5 Jam T OL WaPo dis yu SAS X v COMI NT D BINA eT 1 gt viia mu ML H lt gt a Ane vl n 4 4 le NT DS Y paro x o penes bor FAS M aet oT 20111 4 M welt ert est Rd ADS whl Tt em 9 ace uM y mue WE C EN s rly y Fs f 77 We ATR it ergy y ries GE P TW TNT HD SW rev xk o gt 1 1 rl LU oy 7 3 A Qu yit x tM Ges 3325 pA TT 1 MAN TA
8. Decun Docun Text D Test j k os ido Bam ca GENDER MALE AGE HITOM E Dnusiber 00004 GENDER FEMALE AGE 181029 E N B At the moment in order to ensure the integrated use of various tools each corpus file shouldn t exceed 90 Mb i e about 55 000 pages in txt format For more information see the Requirements and Performances section of the Help Manual Six steps are that is required to perform a quick check of the software functionalities 1 Click on the Select a T LAB demo File option o TLAR THULI FOR TEXT ANALYSIS NETWORK USER TEL ALB T LAB Plus 2019 4 1 0 3 4 MERU START A NEW SESSION xe Selecta T LAB demo file Import a single file txt doc docx pdf rtf Preparelmport more files or tables Corpus Builder i Open an existing project from folder Open an existing project from T LAB list KEY TERMS LIST amp Automatic Customized a Check Change T LAB Plus 2019 Quick Introduction Pag 5 of 29 2 Select any corpus to analyse E 1 La amp ocu 5 FOR TEXT Anat HETWORK USER m ge 2 www tlab it TELIA T LAB Plus 2019 4 1 0 3 gt ANALYSIS PATHS MENU START ANEW SESSHON Select a T LAB demo fila CLICK OF AN ITEM SELECT THE FILE TO BE DEUTSCH 3 Click ok in the first Setup window SETTINGS USER PROFILE Beginner Heian da
9. SCORE 233 SELECT A SUB SET CO TH of course organized religion doesn t have a monopoly on virtue and one not need be religious to make moral appeal to a common good But we should not avoid making such claims or appeals or abandon any refe D TH OPPORTUNITY religious traditions in order to avoid giving offense O TH PARTIES CHAPTER Six THEME FAITH SCORE 231 If am opposed to abortion for religious reasons and seek to pass a law banning the practice I cannot simply teachings of my church or invoke God s will and expect that argument to carry the day If want others to liste I have to explain why abortion violates some principle that is accessible to people of all faiths including those at all CHAPTER Six THEME FAITH SCORE 211 The willingness of many who oppose abortion to make an exception for rape and incest indicates a willingness principle for the sake of practical considerations the willingness of even the most ardent prochoice advocates some restrictions on late term abortion marks a recognition that a fetus is more than a body part and that so interest in its development T LAB Plus 2019 Quick Introduction Pag 18 of 29 T L A B www tlab it info tlab it Correspondence Analysis allows us to explore similarities and differences between and within groups of context units e g documents belonging to the same category X Fact 1 10 36
10. WORD CHECK ENGLISH Yes Basic 9 No O No Advanced O TEXT SEGMENTATION MULTI WORD CHECK ELEMENTARY CONTEXTS Chunks 9 Basic 9 Paragraphs O Advanced O KEY TERM SELECTION IMPORTANCE ORDER METHOD TFADF AUTOMATIC LIST MAX ITEMS CHI SQUARE 3000 gt OCCURRENCES WITH OCCURRENCE VALUE gt 4 HASHTAG OPTIONS e g TWITTER Separate from words e g design design Use the hashtags as they are e g design design C During the pre processing phase T LAB carries out the following treatments Corpus Normalization Multi Word and Stop Word detection Elementary Context segmentation OAutomatic Lemmatization or Stemming Vocabulary building Key Terms selection T LAB Plus 2019 Quick Introduction Pag 9 of 29 www tlab it Here 1s the complete list of the thirty 30 languages for which the automatic lemmatization or the stemming process is supported by T LAB Plus LEMMATIZATION Catalan Croatian English French German Italian Polish Portuguese Romanian Russian Serbian Slovak Spanish Swedish Ukrainian STEMMING Arabic Bengali Bulgarian Czech Danish Dutch Finnish Greek Hindi Hungarian Indonesian Marathi Norwegian Persian Turkish When selecting languages in the setup form while the six languages for which T LAB already supported the automatic lemmatization can be selected trough the button on the left see A below the new o
11. cluster of contexts units characterized by the same patterns of key words see the Thematic Analysis of Elementary Contexts Thematic Document Classification and Dictionary Based Classification tools 2 a thematic group of key terms classified as belonging to the same category see the Dictionary Based Classification tool 3 a mixture component of a probabilistic model which represents each context unit 1 e elementary context or document as generated from a fixed number of topics or themes see the Modeling of Emerging Themes tool 4 a specific key term used for extracting a set of elementary contexts in which it is associated with a specific group of words pre selected by the user see the Key Contexts of Thematic Words tool For example depending on the tool we are using a single document can be analysed as composed of various themes see A below or as belonging to a set of documents concerning the same theme see B below In fact in the case of A each theme can correspond to a word or to a sentence whereas in the case of B a theme can be a label assigned to a cluster of documents characterized by the same patterns of key words A B In detail the ways how T LAB extracts themes are the following both the Thematic Analysis of Elementary Contexts and the Thematic Document Classification tools when performing an unsupervised clustering work in the following way
12. co occurrence relationships determine the local meaning of selected words TERRORIST ASSOCIATIONS ASSOCIATE __ CAMP Right click of the mouse T Chi square Test p lt 05 Viewing Style O TERRORS Font Size O UNDERSTAND Unde Zoom L1 GOVERNMENT coNGRESS O THAME O coni UNITED STA DASE EVIL O SURE O JATTACK INTELLIGENCE SELECT A LA TERRORIST O PEOPLE C AMERICA L AMERICAN E LI NATION Click SEF double click 7 headings to 277 Hl x WORLD Key EC elementary contexts F TERRORIST other values EC A 86 TOT EC 734 O WORK Click on a iem of the table gt HTML OUTPUT EC AB ca accurrences O COUNTRY L SREAT O KN W WAR HELF CO FREEDOM C TERRORISM UNDERSTAND O TIME O STRONG O THANE O HEED O GOVERNMENT HUSH SEP24 As I told the American people we will direct every resource at our command to win the war against terrorists every means of diplomacy every tool of intelligence every m of instrument of law enforcement every financial influence We will starve the funding turn them against each other root them out of their safe hiding places and bring them to justice BUSH SEP24 We have established a foreign terrorist asset tracking center at the Department of the Treasury to identify and investigate the financial infrastructure of
13. the international terrorist networks It will bring together representatives of the intelligence law enforcement and financial regulatory agencies lo accomplish two goals 8 RUSH SEPH we re working closely with the United Nations the EU and through the G 7 G 8 structure to limit the ability of terrorist organizations to take advantage of the intermational 1 8 LT um ik EN F ob m atu 11111 1291 systems 1 0800 110 CALEL CELAL vig IIALELIEES T LAB Plus 2019 Quick Introduction Pag 13 of 29 www tlab it info tlab it Comparison between Word Pairs This T LAB tool allows us to compare sets of elementary contexts 1 e co occurrence contexts in which the elements of a pair of key words are present LEMMA OCC PEOPLE 7 AMERICA LEMMA A LEMMA B LI AMERICAN TERRORIST MUSLIM NATION O WORLD E TERRORIST WORK DI FREEDOM O TERRORISM LI UNDERSTAND O TIME O THANK OSTAONG C NEED CO GOVERNMENT OSURE 1Gn00D CO OCCURRENCES ENN i TERRORIST EU AB BEND B 0 2 T LAB Plus 2019 Quick Introduction Pag 14 of 9 www tlab it Co Word Analysis and Concept Mapping This T LAB tool allows us to find and map co occurrence relationships between sets of key words MDS SAMMON S METHOD STRESS 0 1110 STORA TECHNOLOGY 00 AIR pao E x MATERIAL E PowweRCIA m am
14. 0 55 Leese eod fa qe SX 1 DS po 1 A2 aI HCG il Va vem IID our e Tuo scm e T plz Du U o 2 sa Rii y o yt sog Ab Sa ud PE po tT ker COP AR le v Viertel AP eere at fi air FTT YE ata futni 5 Y a J p Th 0 x LS 5 b es E WT E A alas SNC WA gt pi rr 3 NR DEF A vo QUA Mot AMAS vim M e Va Vp 7 COM owl kiy dias DV Qi VE ty thar fib Gel d 5 PMA VET Au xU DY Faces Art Like Cea alte AA corms D x m 25 e e proe K TASEK ers v LY ej fe Sein poets Do viele min FA ES n WBNS gt can MNE yb Lenis nefie yur icy Wi thu I eRe t we aa Yos2 Y hwy E VEI Ae A Cx a Pag wW 2 X is Thee iy D nnm gt Ao Li LN LUN WP gt aN a 3 1 11114 2 ety 7 23 WAG win ac 7 lt ow uu 1 OP AAN 3 E SAO A i ns Yo ee I7 tie ye Jus c
15. 4 gt be AIL aiit gt ATIS e po a Em i Saad DES ii Brg 2007 001 0 2 gt 7 pv wn oT fw 1 x 4 1 ie ED dx 061 RS ru LE 1 KALF j tm Y 1 tiA omo IZ gm abr v ip 5 gt A PET oy Nene 59 Vae 4 5325 ta A oro f yr 1 fria slo vas aol lA leo PI te Pere ROSE SELAT 13 Mor Cr vv e 1 Yo 1 FT roin wr e vu ift Th FMD fo A RET Af 21 yo Fab 1 Clu d Cn gt A le ala AA Y Un ATO Lo 5 eMe elo 100 S yu The above artwork has been realized for T LAB by Claudio Marini http www claudiomarin1 it in collaboration with Andrea D Andrea OVE TT RSA A Tools for Text Analysis Copyright 2001 2019 T LAB by Franco Lancia All rights reserved Website http www tlab it E mail info tlab it T LAB is a registered trademark www tlab it info tlab it What T LAB does and what it enables us to do Excerpt from the User s Manual T LAB software is an all in one set of linguistic statistical and graphical tools for text analysis which can be used in research fields like Content A
16. ALYSIS FOR FOCUSING ON FOR FOCUSING ON EXPLORING CO OCCURRENCE EXLORING SIMILARITIES AND RELATIONSHIPS BETWEEN DIFFERENCES BETWEEN LEXICAL UNITS CORPUS SUBSETS N B Besides the distinction between tools for co occurrence comparative and thematic analysis it can be useful to consider that some of the latter allow us to obtain new corpus subsets which can be included in further analysis steps Even though the various T LAB tools can be used in any order there are nevertheless three ideal starting points in the system which correspond to the three ANALYSIS sub menus A TOOLS FOR CO OCCURRENCE ANALYSIS These tools enable us to analyse different kinds of relationships between lexical units 1 e words or lemmas RELATIONSHIPS TO BE ANALISED ONE TO ONE Word Associations Sequence Analysis Comparison between Word pairs Co Word Analysis and Concept Mapping Sequence Analysis According to the types of relationships to be analysed the T LAB options indicated in this diagram use one or more of the following statistical tools Association Indexes Chi Square Tests Cluster Analysis Multidimensional Scaling and Markov chains Here are some examples N B for more information on how to interpret the outputs please refer to the corresponding sections of the help manual T LAB Plus 2019 Quick Introduction Pag 12 of 29 www tlab it Word Associations This T LAB tool allows us to check how
17. E j Ax Col SYRIA Qs BILLION ji STOHY NUMBER SHARE e 28 T LAB Plus 2019 Quick Introduction Pag 15 of 29 COMMUNITY d o o A o 5 ro www tlab it info tlab it ENTIRE NETWORK Moreover by clicking the GRAPH MAKER option the user is allowed to obtain various types of graphs by using customized lists of key words see below HII 955596 6 55 264 254 243 239 236 233 MP AJ bi KEE T LAB Plus 2019 Quick Introduction Pag 16 of 29 B TOOLS FOR COMPARATIVE ANALYSIS www tlab it These tools enable us to analyse different kinds of relationships between context units e g documents or corpus subsets RELATIONSHIPS TO BE ANALISED BETWEEN PAIRS ALL TOGETHER Specificity Analysis Correspondence Analysis Cluster Analysis Specificity Analysis enables us to check which words are typical or exclusive of a specific corpus subset either comparing it with the rest of the corpus or with another subset Moreover it allows us to extract the typical contexts i e the characteristic elementary contexts of each analysed subset e g the typical sentences used by any specific political leader SELECT A VARIABLE THEME 5 SELECT A MEASURE SELECT A COMPARISOM r PART WHOLE SELECT A SUB SET L TH CONSTITUTION TH FAITH El TH_ FAMILY NEN CITH PARTIES 1TH PO
18. LITICS 1TH RACE JEE 56 95 103 88 0 000 eer TA Les 139 162 Eme s 1 s E E e beret insurance cee Be 5 5 3E cp EEEBEBEBBEBEEEBE 5 EAE ojal baji 3 MEM Eun Fath FAMILY opp __ PART POLL T LAB Plus 2019 Quick Introduction Pag 17 of 29 B www tlab it info tlab it SELECT A VARLABLE L ABANDON 1 3 0 0 1 3 ABIDE pa 1 gamut ere A I 8 TU 80 ABRAHAM O ABROAD C ABSENCE C ABSOLUTE ABSORB C ABUSE O ACADEMIC O ACCELER CO ACCEPT 1ACCESS I ACCOMPL 8 O ACCOUNT ACCUSATI O ACCUSE Oaccusto auai Ll ACHIEVE 10 O ACHIEVEM SELECT A MEASURE CHI SQUARE SELECT A COMPARISON PART WHOLE _ CI TH FAITH L TH FAMILY m TH OPPORTUNITY OTH_PARTIES 1TH POLITICS x 1TH RACE CO TH vALUES OO TH w RLD CONSTITUTION FAMILY L ACKNOWL FAITH OPPORTUNITY I ACENOW ACTION O ACTIVE L ACTIVIST AIO AT 16 847 08 22 OCCURRENCES Select an item plot your chart click on an item of the table values gt html output P SELECT A VARIABLE E amp ITEM Sst FAITH FAMILY OPPORTU PARTIES 1 SELECT A BIATUFI 1 3 2 SELECT tses CHAPTER Sa THEME FAITH 3
19. MBER I also want to speak tonight directly to Muslims throughout the world We respect your that 7 faith it s practiced freely by many millions of Americans and by millions more in Z H countries that America counts as friends Its teachings are good and peaceful and those who commit evil in the name of Allah blaspheme the name of Allah 6 INTERPRETATION OF THE OUTPUTS consists in the consultation of the tables and the graphs produced by T LAB in the eventual customization of their format and in making inferences on the meaning of the relationships represented by the same In the case of tables according to each case T LAB allows the user to export them in files with the following extensions DAT TXT CSV XLS HTML This means that by using any text editor program and or any Microsoft Office application the user can easily import and re elaborate them All graphs and charts can be zoomed maximized customized and exported in different formats right click to show popup menu CO OCCURRENCES Ww GENETIC AB MEME 8 CROP CO GENE Remove terns C ENGINEER Add items 0 15 20 25 30 35 crop Viewing Style Border Style Plotting Method Data Shadows Grid Options Point Point Label Orientation Area Unde Zoom Bar Stacked Bar Stacked Percent Area Stacked Area Stacked Percent Points Best Fit Line Points Best Fit Line T Points Best Fit Curve Points Best Fit Curve U Points Line TABLE
20. S 1 Points Spline GRAPHS FORMATION HERO Sene SELECT A SUBSET 2 1 1 i junan i 1 1 Line Bar O SCIENTIST TECHNOLOGY O TRANSGENIC PRODUCT O 00 O HERBICIDE O CHILD ORGANISM CREATE BIOTECH ENVIRONMENT O Grow Customization Dialog 8 71 70 68 68 66 64 58 57 55 54 53 53 51 43 AT 47 2 T LAB Plus 2019 Quick Introduction Pag 26 of 29 B www tlab it Customization General Plot Subsets Axs Fork Color Style Graph Attnbules 292 78 8 aera IME C SF 1 Shadow Color E Quick Styles EES E E ea ET ETETETT SETE 1 C oreground V Bitmap Gradient Styles C Graph Background ig Medium Dak Inst Inset C Inset C Shadow f Shadow C Shadow Line C Line C Line MoBomnde No Border lf Tat D md ee ue Exporting E part ic EMF 6 WMF C BMP JPG C PNG C Test Data Export Destination ClipBoard File Browse Printer Export Size ie Pixels C Hillimeters Inches Points Width 1000 45 Pixels OFI 300 Large Fant Cancel Some general criteria for the interpretation of the T LAB outputs are illustrated in a paper quoted in the Bibliography and are available from the www tlab it website Lancia F 2007 This document presents the hypothesis that t
21. S aM METHOD 5 MAX ITEMS Other 9 CHI SQUARE 3000 gt CHI SQUARE 3000 rris 1 I 1 OCCURRENCES WITH OCCURRENCE VALUE gt 4 OCCURRENCES WITH OCCURRENCE Romanian Russian 700 1 a Slova HASHTAG OPTIONS e g TWITTER HASHTAG OPTIONS e g TWITTER Swedish Separate from words e g amp design design nou 7 Use the hashtags as they are e g design design C Hen Un UR NOR In any case without automatic lemmatization and or by using customized dictionaries the user can analyse texts in all languages provided that words are separated by spaces and or punctuation N B As the pre processing options determine both the kind and the number of analysis units 1 e context units and lexical units different choices determine different analysis results For this reason all T LAB outputs i e charts and tables shown in the user s manual and in the on line help are just indicative 3 THE USE OF LEXICAL TOOLS allows us to verify the correct recognition of the lexical units and to customize their classification that 15 to verify and to modify the automatic choices made by T LAB T LAB Plus 2019 Quick Introduction Pag 10 of 29 T L AB www tlab it info tlab it 4 THE KEY WORD SELECTION consists of the arrangement of one or more lists of lexical units words lemmas or categories to be used for producing the data tables to be analysed The automat
22. a perform co occurrence analysis to identify thematic clusters of context units b perform comparative analysis of the profiles of the various clusters c generate various types of graphs and tables see below d allow you to save the new variables thematic clusters for further analysis T LAB Plus 2019 Quick Introduction Pag 21 of 29 www tlab it info tlab it THEMATIC CLUSTERS CLUSTERS X Fact 1 41 009 Y Fact 2 31 2794 CLUSTERS 1 00 1 8 2 franc FACTORIAL ANALYSIS THEMATIC CLUSTERS 1 CLUSTERS VARIABLES eV y PI FACTORIAL ANALYSIS px ini n g T LAB Plus 2019 Quick Introduction Pag 22 of 29 www tlab it THEMATIC CLUSTERS CLUSTERS X 1 41 0046 Fact 2 31 2794 33 emp 102 122H17 8896 0 000 ss 58 95 876 0 000 n FACTORIAL ANALYSIS st 32 EX f SM a ES A 39 1 ee o a ausen mE Ee iw i seed 16 16 27 755 0 i is 26 037 0 00 al pelutian 20 25 189 0 00 come 35 21 495 0 00 _ 26 35 21 496 0 00 LIT 2 through the Dictionary Based Classification tool we can easily build test apply models e g dictionaries of categories or pre existing manual categorizations both
23. ency tables and co occurrences matrices T LAB Plus 2019 Quick Introduction Pag 2 of 29 www tlab it The T LAB user interface 15 very user friendly and various types of texts can be analysed a single text e g an interview a book etc a set of texts e g a set of interviews web pages newspaper articles responses to open ended questions Twitter messages etc All texts can be encoded with categorical variables and or with IDnumbers that correspond to context units or cases e g responses to open ended questions In the case of a single document or a corpus considered as a single text T LAB needs no further work just select the Import a single file option see below and proceed as follows mj TLAR TOOUS FOE TENT ANALYSES NETWORK USER Pius 2049 4 1 0 3 pams MENU START ANEW SESSION Select a T LAB demo file Import a single file bet doc docx p rtf Prepareimport more files or tables Corpus Builder KEY TERMS LIST Open an existing project from folder amp Automatic 53 9 Open an existing project from T LAB list c gt Check I Change PH Frais zem Service IMPORT YOUR FILE WITHOUT VARIABLES Fle cepted file types TXT DOC DOCX POF RTF Other Formats Copp Faste your TEXT in the box below HLE In the cage of PDF files make sure that they are not in an image anly format Preview The pictures of ai
24. for the classical qualitative content analysis and for the sentiment analysis In fact such a tool allows us to perform an automated top down classification of lexical units 1 e words and lemmas or context units 1 e sentences paragraphs and short documents present in a text collection IMPORT XOUR DICTIONARY DICTIONARY confus ACTIVE Jarrai HOSTE EGA _ passive POSITI CATEGORY lt HOSTILE gt OCCURRENCES OF lt ADVERSARY gt PRES REGAN1981 PARTY REP MULTIPLE SELECTION as_for the enemies of freedom those who are potential adversaries they will_be Yes Qro reminded that peace is the highest aspiration of the American people PRES REGAN1981 PARTY REP Itis a weapon our adversaries in today s world do not have PRES CLINTONI997 PARTY DEM Instead now we are building bonds with nations that once were our adversaries PRES OBAMA2009 PARTY DEM Our health care is too costly our schools fail too many and each day brings further evidence that the ways we use energy strengthen our adversaries and threaten our planet T LAB Plus 2019 Quick Introduction Pag 23 of 29 gt gt AUTOMATIC LIST gt gt RENAME CATEGORIES CONTINGENCY TABLES MULTIPLE SELECTION Yes O No PLOT YOUR CHART GRAPHS TT WEN CORRESPONDENCE ANALYSIS EXPORT YOUR DICTIONARY CORRESPONDENCE ANALYSIS FURTHER T LAB ANALYSES OCCURRENCES Right Click Menu O GRATEFUL
25. he statistical elaboration outputs tables and graphs are particular types of texts that is they are multi semiotic objects characterized by the fact that the relationships between the signs and the symbols are ordered by measures that refer to specific codes In other words both in the case of texts written in natural language and those written in the statistical language the possibility of making inferences on the relationships that organize the content forms is guaranteed by the fact that the relationships between the expression forms are not random in fact in the first case natural language the significant units follow on and are ordered in a linear manner one after the other in the chain of the discourse while in the second case tables and graphs the organization of the multidimensional semantic spaces comes from statistical measures Even if the semantic spaces represented in the T LAB maps are extremely varied and each of them require specific interpretative procedures we can theorize that in general the logic of the inferential process 1s the following A to detect some significant relationships between the units present on the expression plan e g between table and or graph labels B to explore and compare the semantic traits of the same units and the contexts to which they are mentally and culturally associated content plan T LAB Plus 2019 Quick Introduction Pag 27 of 29 www tlab it info tlab i
26. i Burieskanger Kol Schroder und Meno Fort Weegee ber Peychenanadbyse ror Freund ir der Lowe von pir uber realen all TE ai dt pasado peor ET Mons que ky cns icone poaa polilica da la morarquin exparicis de 2 XDE aa la Segura iieri ih eee lend La Bokeh Coreen dosis cocti concamani idi 1008 lla qamir ouverte Chun ln chose plus importante onn d oram germ Hakan Bec ogee Diebe dh eee del lala pi hihi dala mua 112 rapes di un di 200951 alis domanda Troes davet r erm partugudia do Exangelo de Joc Testa ers Pongi daa cines bes sobes de agence Freue Teen pea d Ea Bia Fina melo cere Lua Pao Lua de Y NAME Bush Septih txt 151 Kb PATH IAUsersWDocuments T LAB PLUSIDemo em TEXTS VARIABLES 1 IDHUMBERS ENGLISH Absent AUTOMATIC LEMMATIZATION ui Yes a For more information click on the 7 button SHOW MORE OPTIONS 4 Select a tool from one of the Analysis sub menus NETWORK USER m ij Start a new session g CO OCCURRENCE ANALYSIS E THEMATIC ANALY 515 1 COMPARATIVE AMALY SIS dt Lexical Tools Other Tools
27. i Automatic t Customized Open an existing project from T LAB list Check I Change PL EMGLISH 2 Fonti Service Organize gt Hew Folder Xr Favorites Hame Date modified Type Desktop anti 12 01 2013 1002 File canv r Downlaads a example mdb 13 01 2013 12 18 Applicazi Recent Places E exarnplet3 xis 13 01 2013 12 18 Foglio di T sla 13 01 2012 12 18 Foglio di Libraries E samplel accdb 13 01 2013 8 ACCDB F Documents al Music ie Pictures B videos 9 E e s 3 ri Homegroup t Computer ese ar v E AGE _ 1 Table Fides ais apace h Open f Cancel IUnumiber OE GENDI d i Sane 7 Ens people mound T LAB Plus 2019 Quick Introduction Pag 4 of 29 www tlab it DESIT EDT DC BUT BTE GENDER 5 n l Hane 7 file file TXT E 6188 007 EJ files DOC 8 7 ii Filed DOCK ii fied OCH 0 7 filel2 TXT Date modified 05 03 2002 22 12 05 03 2007 22 14 05 03 2002 22412 05 03 2002 22 17 05 03 2002 22412 05 03 2002 22 17 05 03 2002 22 12 05 03 2002 22 12 05 03 2007 225 14 05 03 2002 22 12 05 03 2002 22 17 05 03 2002 22412 Type amp Text D Fett Ls Text D Docun Decun 1 B RTF Ri L Docuni
28. ic settings option provides the lists of the key words selected by T LAB nevertheless since the choice of the analysis units is extremely relevant in relation to subsequent elaborations the use of customized settings see below is highly recommended In this way the user can choose to modify the list suggested by T LAB and or to arrange lists that better correspond to the objectives of his research EY WORDS LEMMAS OR CATEGORIES SELECT REN t Ri ITEM GENETIC GENE Right Click ENGINEER GENETICALLY crop HUMAN LIFE PLANT FOOD SCIENCE COMPANY ANIMAL SCIENTIST TECHNOLOGY TRANSGENIC PRODUCT WORLD MERBICIDE 5 THE USE OF ANALYSIS TOOLS allows the user to obtain outputs tables and graphs that represent significant relationships between the analysis units and enables the user to make inferences At the moment T LAB includes fifteen different analysis tools each of them having its own specific logic that 15 each one generates specific tables uses specific algorithms and produces specific outputs Consequently depending on the structure of texts to be analysed and on the goals to be achieved the user has to decide which tools are more appropriate for their analysis strategy every time T LAB Plus 2019 Quick Introduction Pag 11 of 29 www tlab it FOR FOCUSING ON GROUPING WORDS AND OR DOCUMENTS INTO THEMATIC CLUSTERS THEMATIC ANALYSIS CO OCCURRENCE COMPARATIVE ANALYSIS AN
29. nalysis Sentiment Analysis Semantic Analysis Thematic Analysis Text Mining Perceptual Mapping Discourse Analysis Network Text Analysis Document Clustering Text Summarization THEMATIC ANALYSIS CO OCCURRENCE COMPARATIVE ANALYSIS ANALYSIS In fact T LAB tools allow the user to easily manage tasks like the following e measure explore and map the co occurrence relationships between key terms e perform either unsupervised or supervised clustering of textual units and documents 1 e perform a bottom up clustering which highlights emerging themes or a perform top down classification which uses a set of predefined categories e check the lexical units i e words or lemmas context units 1 e sentences or paragraphs and themes which are typical of specific text subsets e g newspaper articles from specific time periods interviews with people belonging to the same category apply categories for sentiment analysis e perform various types of correspondence analysis and cluster analysis e create semantic maps that represent dynamic aspects of the discourse 1 e sequential relationships between words or themes represent and explore any text as a network e customize and apply various types of dictionaries for both lexical and content analysis e perform concordance searches e analyse all the corpus or its subsets e g groups of documents by using various key term lists create explore and export numerous conting
30. ne can be selected trough the button on the right see B below English French German Italian Portuguese and Spanish T LAB PRE PROCESSING OF THE CORPUS gt BUSH SEPTTI TXT gt T LAB PRE PROCESSING OF THE CORPUS gt BUSH SEPTIT TXT gt CORPUS CORPUS HAME MyCorpus txt NAME MyCorpus txt our 1285 Kb DIMENSION 1285 Kb PATH G OMyCorpusi PATH GrMyCorpus TEXTS lt ONLY OHE gt TEXTS gt ONLY OWE gt VARIABLES 0 VARIABLES 0 A IDMUMBERS Absent IDMUMBERS Absent B AUTOMATIC LEMMATIZATION gt Yes Mo C OTHER v CHOOSE ONE Arabic Arabic A ITALIANO A oe Bengali FRAN A 1 lick on the 7 button 22 gi For more information click on the 7 button Bulgarian ESPANOL Catalan PORTUGU S STOP WORD CHECK STEMMING STOP WORD Chinese Yes Basie AVAILABLE M 5 1 LT Ani No C No Advanced Lome Dutch i Finnish TEXT SEGMENTATION MULTI WORD CHECK TEXT SEGMENTATION MULTI WORD Greek ELEMENTARY CONTEXT 5 ELEMENTARY CONTEXT S Hebrew Sentences No C Sentences l 1 cm ink A LIFES hunks Basi zx 5 Indonesian Paragraphs C Advanced Paragrapha Japanese l Korean 1 s ese BA 5 i i Latvian KEY TERM SELECTION IMPORTANCE ORDER 6 s eL Lei Marathi T Norwegian METHOD TFJDF MAX ITEM
31. o the interpretation of the outputs are supported by T LAB tools and are always reversible By using T LAB automatic settings it is possible to avoid two phases 3 and 4 however in order to achieve high quality results their use is nevertheless advisable Now let s try to comment on the various steps 1 CORPUS PREPARATION transformation of the texts to be analysed in a file corpus that can be processed by the software In the case of a single text or a corpus considered as a single text T LAB needs no further work When on the other hand the corpus is made up of various texts and or categorical variables are used the Corpus Builder tool must be used which automatically transforms any textual material and various types of files 1 e up to eleven different formats into a corpus file ready to be imported by T LAB 2 CORPUS IMPORTATION a series of automatic processes that transform the corpus into a set of tables integrated in the T LAB database Starting from the selection of the Import a Corpus option the intervention of the user is required in order to define certain choices see below T LAB PRE PROCESSING OF THE CORPUS gt BUSH SEPT11 TXT gt NAME Bush Septf4 txt DIMENSION 151 Kb PATH C Users iDocuments T LAB PLUS Demo_en TEXTS 22 PRIMARY DOCUMENTS VARIABLES 1 IDNUMBERS Absent ENGLISH v AUTOMATIC LEMMATIZATION Yes No O For more information click on the button x Q LEMMATIZATION STOP
32. r elementary contexts Word associations comparison between word pairs max 5 000 lexical units co word analysis and concept mapping max 5 000 lexical units e sequence analysis max 5 000 lexical units or categories by 3 000 000 occurrences In T LAB lexical units are words multi words lemmas and semantic categories So when the automatic lemmatization is applied 5 000 lexical units correspond to about 12 000 words 1 e raw forms T LAB Plus 2019 Quick Introduction Pag 29 of 29
33. rplanes flying into buildings fires burning huge structures collanasing have filed us wih debatet temible anger Today our nation saw evil the very worst of human nature is pa apr ire with the daring of our rescue workers wilh the caring Tor strangers and neighbors who came to give blood and help in arr way they could implemented our gevemment s emergency response plans Our miktar and i s prepared Our gr Emergency teams ane working in Hew York City and Washington D C to help with local rescue efforts a a RE c i UMP m E y pcc E cic Han NEM M vm father attacks Federal agencies in Washington which had to be evacuatec T LAB Plus 2019 Quick Introduction Pag 3 of 29 www tlab it When on the other hand the corpus is made up of various texts and or categorical variables are used the Corpus Builder tool see below must be used In fact such a tool automatically transforms any textual material and various types of files 1 e up to ten different formats into a corpus file ready to be imported by T LAB o TLAR TOOLS FOR TEXT NETWORK USER TOLAB gt ANALYSIS PATHS sernus MENU START A HEW SESSION USER PROFILE Select a T LAB demo file 9 Beginner Import a single file txt doc docx pdt rtf Expert Prepar import more fles or tables Corpus Builder KEY TERMS LIST Open an existing project from folder
34. s us to check how E Sequence Analysis co occurrence relationships determine the 8 Concordances local meaning of selected words De Thematic Analysis Comperstive Analysis Onthe left there is the table with the key Leal Tools term list and their occurrence values within 117 165 150 134 125 114 100 90 79 77 TI amp 8 b4 H ae uem 5 5 5 5 Ltiities the whole corpus or a subset of it Glossary Ga Biblografia On user request a simple click for each key term T LAB shows the lexical units that share co occurrence contexts with that key word The selection is carried out by the computation of an Association Index Cosine Dice and Jaccard Lets consider how a typical work project which uses T LAB can be managed Hypothetically each project consists of a set of analytical activities operations which have the same corpus as their subject and are organized according to the user s strategy and plan It then begins gathering the texts to be analysed and concludes with a report The succession of the various phases is illustrated in the following diagram TEXT GATHERING _1 CORPUS PREPARATION 2 CORPUS IMPORTATION 3 USE OF LEXICAL TOOLS 4 KEY WORDS SELECTION 5 USE OF ANAL TS YS TOOLS 6 OUTPUT INTERPRETATION REPORT EDITING T LAB Plus 2019 Quick Introduction Pag 8 of 29 www tlab it N B The six numbered phases from the corpus preparation t
35. t C to generate some hypothesis or some analysis categories that in the context defined by the corpus give reason for the relationships between expression and content forms T LAB Plus 2019 Quick Introduction Pag 28 of 29 www tlab it At present T LAB Plus options have the following restrictions e corpus dimension max 90Mb equal to about 55 000 pages in txt format e primary documents max 30 000 max 99 999 for short texts which do not exceed 2 000 characters each e g responses to open ended questions Twitter messages etc e categorical variables max 50 each allowing max 150 subsets categories which can be compared modelling of emerging themes max 5 000 lexical units by 5 000 000 occurrences e thematic analysis of elementary contexts max 300 000 rows context units by 5 000 columns lexical units e thematic document classification max 30 000 rows context units by 5 000 columns lexical units e specificity analysis lexical units x categories max 10 000 rows by 150 columns e correspondence analysis lexical units x categories max 10 000 rows by 150 columns e correspondence analysis context units x lexical units max 10 000 rows by 5 000 columns multiple correspondence analysis elementary contexts x categories max 150 000 rows by 250 columns e cluster analysis that uses the results of a previous correspondence analysis max 10 000 rows lexical units o
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