Data Visualization: Language Variation Suite and Interactive Text Mining Suite
•
7 likes•3,412 views
This workshop introduces two user-friendly applications, namely Language Variation Suite and Interactive Text Mining Suite, that allow researchers visually explore and statistically analyze language data. Written in R with Shiny app, these applications not only provide a web interactive interface, they also allow researchers implement state-of-the-art statistical methods, such as cluster analysis, topic modeling, inferential trees and mixed model logistic regressions.
Recommended
Recommended
More Related Content
Similar to Data Visualization: Language Variation Suite and Interactive Text Mining Suite
Similar to Data Visualization: Language Variation Suite and Interactive Text Mining Suite (20)
More from Olga Scrivner
More from Olga Scrivner (20)
Recently uploaded
Recently uploaded (20)
Data Visualization: Language Variation Suite and Interactive Text Mining Suite
- 1. Introduction Language Variation Suite Visual Analytics for Digital Humanities Interactive Text Mining Suite Conclusion References Data Visualization: Language Variation Suite and Interactive Text Mining Suite Olga Scrivner Indiana University LSU, April 2016 1 / 54
- 2. Introduction Language Variation Suite Visual Analytics for Digital Humanities Interactive Text Mining Suite Conclusion References Data Analysis and Visualization “As our collective knowledge continues to be digitized and stored (...) it becomes more difficult to find and discover what we are looking for.” (Blei 2012) 2 / 54
- 3. Introduction Language Variation Suite Visual Analytics for Digital Humanities Interactive Text Mining Suite Conclusion References Data Analysis and Visualization “As our collective knowledge continues to be digitized and stored (...) it becomes more difficult to find and discover what we are looking for.” (Blei 2012) “Mastery of quantitative methods is increasingly becoming a vital component of linguistic training” (Johnson, 2008) 2 / 54
- 4. Introduction Language Variation Suite Visual Analytics for Digital Humanities Interactive Text Mining Suite Conclusion References Data Types 1 Structured Data 2 Unstructured Data 3 / 54
- 5. Introduction Language Variation Suite Visual Analytics for Digital Humanities Interactive Text Mining Suite Conclusion References Quantitative Analysis for Structured Data Traditional Tools Linguistic Data a. Categorical variable b. Independence of observation c. Normally distributed data d. Large corpus size a. Categorical, continuous, multivariate, ordinal b. Correlated data c. Unbalanced data d. Small corpus size 4 / 54
- 6. Introduction Language Variation Suite Visual Analytics for Digital Humanities Interactive Text Mining Suite Conclusion References Data Visualization Word Order in Latin (Passarotti et al., 2013) Visual Analytics - “The science of analytical reasoning facilitated by visual interactive interfaces” (Thomas et al., 2005) 5 / 54
- 7. Introduction Language Variation Suite Visual Analytics for Digital Humanities Interactive Text Mining Suite Conclusion References New Tools of Linguistic Analysis (Baayen 2008, Tagliamonte 2014, Gries 2015) 1 Mixed Model: A statistical regression model containing fixed effects (independent variables) and random effects (e.g., individual- or word-specific effects). Measures variability between subjects and correlation of observation within subjects Can handle unbalanced data 6 / 54
- 8. Introduction Language Variation Suite Visual Analytics for Digital Humanities Interactive Text Mining Suite Conclusion References New Tools of Linguistic Analysis (Baayen 2008, Tagliamonte 2014, Gries 2015) 2 Conditional inference trees and Random Forests Uses predictive modeling “Proves to be more stable than stepwise variable selection approaches available for logistic regression” (Strobl 2009:325) Can handle skewed data that often violate the assumptions of regression approaches 7 / 54
- 9. Introduction Language Variation Suite Visual Analytics for Digital Humanities Interactive Text Mining Suite Conclusion References RStudio and Shiny Application 1 R - a free programming language for statistical computing and graphics 2 RStudio - Integrated Development Environment: a source code editor, an executor and a debugger 3 Shiny App - a web application framework for R Computational power of R + Web interactivity 8 / 54
- 10. Introduction Language Variation Suite Visual Analytics for Digital Humanities Interactive Text Mining Suite Conclusion References Language Variation Suite (LVS) - a Statistical Shiny Application 1 From https://languagevariationsuite.wordpress.com/ download Labov’s data New York 1966(LabovData.csv) and Caracas data Bentivoglio & Sedano 1993 (CaracasData.csv) 2 Open LVS application https://languagevariationsuite.shinyapps.io/Pages 9 / 54
- 11. Introduction Language Variation Suite Visual Analytics for Digital Humanities Interactive Text Mining Suite Conclusion References Language Variation Suite - Introduction 1 Data in csv format (no spaces in column names) 10 / 54
- 12. Introduction Language Variation Suite Visual Analytics for Digital Humanities Interactive Text Mining Suite Conclusion References Files 2 Upload your file 11 / 54
- 13. Introduction Language Variation Suite Visual Analytics for Digital Humanities Interactive Text Mining Suite Conclusion References Descriptive Data Analysis - Table Table displays your dataset and allows for filtering columns by a search word, or in descending/ascending order. 12 / 54
- 14. Introduction Language Variation Suite Visual Analytics for Digital Humanities Interactive Text Mining Suite Conclusion References Summary Summary provides a quantitative summary for each variable, ex. frequency count, mean, median. 13 / 54
- 15. Introduction Language Variation Suite Visual Analytics for Digital Humanities Interactive Text Mining Suite Conclusion References Data Structure 1 factor - categorical values, ex. m/f (gender), 20-34/65+ (age), low/high (economic level) 2 num - numerical values, ex. 0.95, 1.5 3 int - integer values, ex. 1, 2, 10 14 / 54
- 16. Introduction Language Variation Suite Visual Analytics for Digital Humanities Interactive Text Mining Suite Conclusion References Data Subset 15 / 54
- 17. Introduction Language Variation Suite Visual Analytics for Digital Humanities Interactive Text Mining Suite Conclusion References Cross-Tabulation Cross-tabulation is a useful feature to examine the distribution of your dependent variable. 16 / 54
- 18. Introduction Language Variation Suite Visual Analytics for Digital Humanities Interactive Text Mining Suite Conclusion References Cross-Tabulation Saks (upper middle-class store), Macy’s (middle-class store), Klein (working-class)17 / 54
- 19. Introduction Language Variation Suite Visual Analytics for Digital Humanities Interactive Text Mining Suite Conclusion References Cluster Cluster Analysis allows you to classify your data into sub-groups (clusters), which are defined by your data. Items in the same cluster will be very similar to one another. Saks (upper middle-class store), Macy’s (middle-class store), Klein (working-class)18 / 54
- 20. Introduction Language Variation Suite Visual Analytics for Digital Humanities Interactive Text Mining Suite Conclusion References LVS - Inferential Analysis Fixed Regression Model - ignoring individual variations (speakers or words) may lead to Type I Error: “a chance effect is mistaken for a real difference between the populations” Mixed Regression Model - prone to Type II Error: “if speaker variation is at a high level, we cannot discern small population effects without a large number of speakers” (Johnson 2009, 2015) 19 / 54
- 21. Introduction Language Variation Suite Visual Analytics for Digital Humanities Interactive Text Mining Suite Conclusion References Regression Model Selection 20 / 54
- 22. Introduction Language Variation Suite Visual Analytics for Digital Humanities Interactive Text Mining Suite Conclusion References Model Output 21 / 54
- 23. Introduction Language Variation Suite Visual Analytics for Digital Humanities Interactive Text Mining Suite Conclusion References Interpretation Dependent Variable: deletion and retention By default - deletion is a reference value (alphabetically) Results are interpreted for retention 22 / 54
- 24. Introduction Language Variation Suite Visual Analytics for Digital Humanities Interactive Text Mining Suite Conclusion References Interpretation Lexical item Fourth has a negative effect on retention and is significant Normal style has a slightly negative effect on retention but its coefficient is not significant Macy’s and Saks have a positive and significant effect on retention. Saks (upper middle class store) is more significant than Macy’s (middle class store) 23 / 54
- 25. Introduction Language Variation Suite Visual Analytics for Digital Humanities Interactive Text Mining Suite Conclusion References Conditional Tree 24 / 54
- 26. Introduction Language Variation Suite Visual Analytics for Digital Humanities Interactive Text Mining Suite Conclusion References Conditional Tree Store is the most significant factor for R-use: Kleins (working class store) - more R-deletion; Macy’s and Saks have a higher rate of R-retention, which also depends on the lexical item (Floor shows more retention than Fourth)25 / 54
- 27. Introduction Language Variation Suite Visual Analytics for Digital Humanities Interactive Text Mining Suite Conclusion References Random Forest 26 / 54
- 28. Introduction Language Variation Suite Visual Analytics for Digital Humanities Interactive Text Mining Suite Conclusion References Random Forest The variable importance score demonstrates that Store is the most important predictor, followed by Lexical Item. The variable is irrelevant is its importance is around the zero and the cut-off value (red dotted line). 27 / 54
- 29. Introduction Language Variation Suite Visual Analytics for Digital Humanities Interactive Text Mining Suite Conclusion References Data with Token Frequency Upload CaracasData.csv from https://languagevariationsuite.wordpress.com/ 28 / 54
- 31. Introduction Language Variation Suite Visual Analytics for Digital Humanities Interactive Text Mining Suite Conclusion References Let’s Have a Short Break 30 / 54
- 32. Introduction Language Variation Suite Visual Analytics for Digital Humanities Interactive Text Mining Suite Conclusion References Visual Analytics for Digital Humanities The “epic transformation of archives” - shifting from print to digital archival form (Folsom, 2007) 31 / 54
- 33. Introduction Language Variation Suite Visual Analytics for Digital Humanities Interactive Text Mining Suite Conclusion References Digital Humanity Manifesto 2.0 (2009) and Berry (2011) 1st Wave: “The first wave of digital humanities work was quantitative, mobilizing the search and retrieval powers of the database, automating corpus linguistics, stacking hypercards into critical arrays” 2nd Wave: “The second wave is qualitative, interpretive”, concentrating on new tools for creating and curating digital repositories (Berry, 2011) 3rd Wave: Concentration on the computationality, search, retrieval and analysis originated in humanity-based work 32 / 54
- 34. Introduction Language Variation Suite Visual Analytics for Digital Humanities Interactive Text Mining Suite Conclusion References New Ways of Exploring Data Collections Graphs, maps and trees for literature analysis (Moretti, 2005) 33 / 54
- 35. Introduction Language Variation Suite Visual Analytics for Digital Humanities Interactive Text Mining Suite Conclusion References Visual Analytics Word clouds to analyze a novel (Vuillemot et al., 2009) 34 / 54
- 36. Introduction Language Variation Suite Visual Analytics for Digital Humanities Interactive Text Mining Suite Conclusion References Visual Analytics Social network graphs of characters in Greek tragedies (Rydberg-Cox, 2011) 35 / 54
- 37. Introduction Language Variation Suite Visual Analytics for Digital Humanities Interactive Text Mining Suite Conclusion References Visual Analytics Literary fingerprint and summaries (Oelke et al., 2012) 36 / 54
- 38. Introduction Language Variation Suite Visual Analytics for Digital Humanities Interactive Text Mining Suite Conclusion References Visual Analytics Tracking emotion and sentiment in fairy tales (Mohammad, 2012) 37 / 54
- 39. Introduction Language Variation Suite Visual Analytics for Digital Humanities Interactive Text Mining Suite Conclusion References Topic Modeling Discovering underlying theme of collection from Science magazine 1990-2000 (Blei, 2012) For more information on topic modeling: http://www.matthewjockers.net/2011/09/29/ the-lda-buffet-is-now-open-or-latent-dirichlet-allocation-for-english-majors/38 / 54
- 40. Introduction Language Variation Suite Visual Analytics for Digital Humanities Interactive Text Mining Suite Conclusion References Interactive Text Mining Suite - Introduction 1 Download 3 text files (dante01.txt, dante02.txt, dante03.txt) from https://languagevariationsuite.wordpress.com/ (workshop) 2 ITMS Application: https://languagevariationsuite.shinyapps.io/ TextMining/ 39 / 54
- 41. Introduction Language Variation Suite Visual Analytics for Digital Humanities Interactive Text Mining Suite Conclusion References Upload Files - txt 40 / 54
- 43. Introduction Language Variation Suite Visual Analytics for Digital Humanities Interactive Text Mining Suite Conclusion References Metadata ID Date Title Author Other Extract from pdf files Upload from csv file 42 / 54
- 46. Introduction Language Variation Suite Visual Analytics for Digital Humanities Interactive Text Mining Suite Conclusion References Frequency Visualization 45 / 54
- 47. Introduction Language Variation Suite Visual Analytics for Digital Humanities Interactive Text Mining Suite Conclusion References More Stopwords 46 / 54
- 48. Introduction Language Variation Suite Visual Analytics for Digital Humanities Interactive Text Mining Suite Conclusion References Topic Modeling Selection of topics (how many different themes) Selection of words per theme (how many words per topic) Identification of the best topic number 47 / 54
- 49. Introduction Language Variation Suite Visual Analytics for Digital Humanities Interactive Text Mining Suite Conclusion References Models LDA (Latent Dirichlet allocation) STM (Structural Topic model) Chronological topic visualization (lda): requires metadata 48 / 54
- 50. Introduction Language Variation Suite Visual Analytics for Digital Humanities Interactive Text Mining Suite Conclusion References Cluster Analysis 49 / 54
- 51. Introduction Language Variation Suite Visual Analytics for Digital Humanities Interactive Text Mining Suite Conclusion References Punctuation Analysis 50 / 54
- 52. Introduction Language Variation Suite Visual Analytics for Digital Humanities Interactive Text Mining Suite Conclusion References Future Directions 1 New LVS features: (a) Traditional Rrbrul analysis (for comparison) (b) Variable re-coding and dataset modification 2 New ITMS features: (a) Network graphs (b) Dynamic graphs 51 / 54
- 53. Introduction Language Variation Suite Visual Analytics for Digital Humanities Interactive Text Mining Suite Conclusion References Acknowledgements I would like to thank Professor Rafael Orozco, Professor Irina Shport and LSU Linguistics for inviting me and organizing this workshop. 52 / 54
- 54. Introduction Language Variation Suite Visual Analytics for Digital Humanities Interactive Text Mining Suite Conclusion References References I [1] Baayen, Harald. 2008. Analyzing linguistic data: A practical introduction to statistics. Cambridge: Cambridge University Press [2] Bentivoglio, Paola and Mercedes Sedano. 1993. Investigaci´on socioling¨u´ıstica: sus m´etodos aplicados a una experiencia venezolana. Bolet´ın de Ling¨u´ıstica 8. 3-35 [3] Gries, Stefan Th. 2015. Quantitative designs and statistical techniques. In Douglas Biber Randi Reppen (eds.), The Cambridge Handbook of English Corpus Linguistics. Cambridge: Cambridge University Press [4] Jockers, Matthew. 2014. Text Analysis with R for Students of Literature. Quantitative Methods in the Humanities and Social Sciences. Springer International Publishing, Cham [5] Labov, W. 1966. The Social Stratification of English in New York City. Washington: Center for Applied Linguistics [6] Moretti, Franco. 2005. Graphs, Maps, Trees: Abstract Models for a Literary History. Verso [7] Oelke, Daniella, Dimitrios Kokkinakis, and Mats Malm. 2012. Advanced visual analytics methods for literature analysis. Proceedings of the 6th EACL Workshop on Language Technology for Cultural Heritage, Social 561Sciences, and Humanities, pages 3544 [8] Passarotti, Marco, Barbara McGillivray, and David Bamman. “A Treebank-based Study on Latin Word Order.” In proceedings of 16th International Colloquium on Latin Linguistics, At Uppsala, Sweden. 2013, 340–352 [9] Schnapp, Jeffrey, and Peter Presner. 2009. Digital Humanities Manifesto 2.0. [10] http://blog.kandu.com/post/57065268403/book-reading-gif [11] http://cdn.business2community.com/wp-content/uploads/2014/09/archives01.jpg 53 / 54