The document discusses interactive text mining and visualization tools for digital humanities research. It introduces the Interactive Text Mining Suite (ITMS), a web application created with R and Shiny that allows users to import texts, extract metadata, perform preprocessing, and generate dynamic visualizations. The document also presents a case study using ITMS to analyze a medieval Occitan corpus, demonstrating visualizations for part-of-speech comparisons, keyword analysis, stylistic similarities, document clustering, and topic modeling. The goal of ITMS is to provide a user-friendly interface for both close and distant reading through interactive statistical analysis and visualization of literary texts.

1. Introduction
Visualization
Methods
ITMS
Medieval
Corpus
Conclusion
Interactive Text Mining Suite: Data
Visualization for Literary Studies
Olga Scrivner and Jefferson Davis
Indiana University
CDH 2017
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2. Introduction
Visualization
Methods
ITMS
Medieval
Corpus
Conclusion
Outline
1 Visual Analytics in Digital Humanities
2 Shiny Web Application - Interactive Text Mining Suite
3 Case Study: Visualization of Medieval Romance of
Flamenca
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3. Introduction
Visualization
Methods
ITMS
Medieval
Corpus
Conclusion
Digital Humanities - Transformation
The “epic transformation of archives” - shifting from print to
digital archival form (Folsom, 2007)
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4. Introduction
Visualization
Methods
ITMS
Medieval
Corpus
Conclusion
Digital Humanities
“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)
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5. Introduction
Visualization
Methods
ITMS
Medieval
Corpus
Conclusion
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
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6. Introduction
Visualization
Methods
ITMS
Medieval
Corpus
Conclusion
Visual Analytics in Literature
“The science of analytical reasoning facilitated by
visual interactive interfaces”
(Thomas et al., 2005)
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7. Introduction
Visualization
Methods
ITMS
Medieval
Corpus
Conclusion
Close Reading
Concept Micro-analysis (Jockers, 2013)
Close textual analysis of individual texts to
“unveil words, verbal images, elements of style,
sentences, argument patterns” (Jasinski, 2001)
Methods Color coding, marginal comments, underlining
Tools Poem Viewer, PRISM, Juxta, eMargin
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8. Introduction
Visualization
Methods
ITMS
Medieval
Corpus
Conclusion
Close Reading Visualization: eMargin and JUXTA
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9. Introduction
Visualization
Methods
ITMS
Medieval
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Conclusion
Distant Reading
Concept Macro-analysis (Jockers, 2013)
“the construction of abstract models”
(Jasinski, 2001)
Methods Tag clouds, heat maps, clusters, topics, network
graphs
Tools GUI: Voyant, Papermachine
TUI: Mallet, Meta, R and Python packages
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10. Introduction
Visualization
Methods
ITMS
Medieval
Corpus
Conclusion
Visualization Methods in Literature
Graphs, maps and trees for literature analysis (Moretti, 2005)
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11. Introduction
Visualization
Methods
ITMS
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Conclusion
Visualization Methods in Literature
Word clouds to analyze a novel (Vuillemot et al., 2009)
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12. Introduction
Visualization
Methods
ITMS
Medieval
Corpus
Conclusion
Visualization Methods in Literature
Social network graphs of characters in Greek tragedies
(Rydberg-Cox, 2011)
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13. Introduction
Visualization
Methods
ITMS
Medieval
Corpus
Conclusion
Visualization Methods in Literature
Literary fingerprint and summaries (Oelke et al., 2012)
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14. Introduction
Visualization
Methods
ITMS
Medieval
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Conclusion
Visualization Methods in Literature
Tracking emotion and sentiment in fairy tales
(Mohammad, 2012)
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15. Introduction
Visualization
Methods
ITMS
Medieval
Corpus
Conclusion
Topic Modeling
Discovering underlying theme of collection from Science magazine
1990-2000 (Blei 2012)
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16. Introduction
Visualization
Methods
ITMS
Medieval
Corpus
Conclusion
Technological and Methodological Obstacles
Many tools require some programming skills (Mallet,
Meta, R and Python libraries)
GUI tools are limited to certain formats and functions
(Voyant, PaperMachine)
Lack of active control by users
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17. Introduction
Visualization
Methods
ITMS
Medieval
Corpus
Conclusion
Our Goals - Interactive Text Mining Suite
A user-friendly interactive tool for quantitative and
visualization analysis
Designed for linguistic and literary analysis
Incorporation of annotated corpora in macro-analysis
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18. Introduction
Visualization
Methods
ITMS
Medieval
Corpus
Conclusion
Background
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
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19. Introduction
Visualization
Methods
ITMS
Medieval
Corpus
Conclusion
ITMS
Platform-independent, user-friendly and interactive
State-of-the-art statistical and graphical tools (R libraries)
http://www.interactivetextminingsuite.com
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20. Introduction
Visualization
Methods
ITMS
Medieval
Corpus
Conclusion
Multi-Functional
1 Import txt, pdf, rdf and Google books API
2 Metadata extraction
3 Interactive data pre-processing
4 Dynamic visualization
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21. Introduction
Visualization
Methods
ITMS
Medieval
Corpus
Conclusion
Case Study - Medieval Occitan
Occitan (Proven¸cal) constitutes an important element of the
literary, linguistic, and cultural heritage in the history of
Romance languages
Interactive online database and linguistically annotated corpus
(Scrivner et al., 2014)
http://www.oldoccitancorpus.org
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22. Introduction
Visualization
Methods
ITMS
Medieval
Corpus
Conclusion
Comparative Analysis of POS: Original and
Translation
Occitan corpus English translation
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23. Introduction
Visualization
Methods
ITMS
Medieval
Corpus
Conclusion
Key-Word-in-Context Analysis of POS
Existential - there Negation
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24. Introduction
Visualization
Methods
ITMS
Medieval
Corpus
Conclusion
Stylistic Similarities - Sentence Length
Occitan Corpus English Translation
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25. Introduction
Visualization
Methods
ITMS
Medieval
Corpus
Conclusion
Stylistic Comparison - Punctuation
Occitan Corpus English Translation
Question marks and exclamation marks - red; quotation marks, hyphens and parenthesis - green; semicolons,
colons, commas, periods - blue25 / 30

26. Introduction
Visualization
Methods
ITMS
Medieval
Corpus
Conclusion
Document Level Cluster Analysis
Cluster analysis - groups documents into subgroups. These
subgroups “are coherent internally, but clearly different from
each other”
(Manning, 2009)
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27. Introduction
Visualization
Methods
ITMS
Medieval
Corpus
Conclusion
Document Level Topic Analysis
Text collections - “represented as random mixtures over latent
topics, where each topic is characterized by a distribution over
words”
(Blei, 2003)
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28. Introduction
Visualization
Methods
ITMS
Medieval
Corpus
Conclusion
Conclusion
1 There is a need for text mining tools designed for linguists
and literary scholars
2 Interactive user-friendly applications bridge the gap
between data mining and digital humanities
3 Shiny framework can be incorporated in any digital
corpora to exhibit, search or visualize written collections
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29. Introduction
Visualization
Methods
ITMS
Medieval
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Conclusion
ITMS
Browser and Smart Phone
Questions, comments
https://languagevariationsuite.wordpress.com/
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30. Introduction
Visualization
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References
Mohammad, Saif. 2013. From Once Upon a Time to Happily Ever After:
Tracking Emotions in Novels and Fairy Tales. In Proceedings of the ACL
Workshop on Language Technology for Cultural Heritage, Social Sciences, and
Humanities (LaTeCH), 2011, Portland, OR.
Moretti, Franco. 2005. Graphs, maps, trees: abstract models for a literary history.
R.R. Donnelley & Sons.
Oelke, Daniela, Dimitrios Kokkinakis and Mats Malm. 2012. Advanced Visual
Analytics Methods for Literature Analysis. In Proceedings of the 6th EACL
Workshop, 35-44.
Rydberg-Cox, Jeff. 2011. Social Networks and the Language of Greek Tragedy.
Journal of the Chicago Colloquium on Digital Humanities and Computer Science.
1(3): 1-11.
Thomas, James and Kristin Cook. 2005. Illuminating the Path: the Research and
Development Agenda for Visual Analytics. National Visualization and Analytics
Center.
Vuillemot, Romain, Tanya Clement, Catherine Plaisant and Amit Kumar. 2009.
What’s Being Near “Martha”? Exploring Name Entities in Literary Text
Collections. In Proceedings if the IEEE Symposium. Atlantic City, New Jersey.
107-114.
http://www.clipartbest.com/clipart-9i4A55xiE
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