HIGH-LEVEL TEXT ANALYSIS AND TECHNIQUESAngela ZossData Visualization Coordinator226 Perkins Libraryangela.zoss@duke.edu

Duke University Libraries, Digital ScholarshipText > Data, October 25

DOCUMENTS AS CONTEXT

ANGELA AS CONTEXTBut first,

How I learned to love the document.B.A. courses: Linguistics, Communication

M.S. courses: Communication, Human-Computer Interaction

Employment: arXiv.org Administrator

Ph.D. courses: •Bibliometrics/Scientometrics•Computer Mediated Discourse Analysis•Latent Structure Analysis•Natural Language Processing

DOCUMENTS AS CONTEXTNow,

Text analysis from…

• documents down to words (“low-level”)

• words up to documents (“high-level”)

Using documents to learn about language (or other social phenomena)

Analyzing documents as records/proxies of language, social structures, events, etc.

Linguistic studies: morphology, word counts, syntax, etc. …

over time (e.g., Google ngram viewer) language across corpora (e.g., political speeches)

Underwood, T. (2012). Where to start with text mining.

Using documents to learn about language

Historical culturomics of pronoun frequencies

Using documents to learn about language

Universal properties of mythological networks

Using language to learn about documents

Analyzing documents as artifacts themselves, with their own properties and dynamics

Literary, documentary studies:Structural/rhetorical/stylistic analysisDocument categorization, classificationDetecting clusters of document features (topic modeling)

Underwood, T. (2012). Where to start with text mining.

Using language to learn about documents

Literary Empires, Mapping Temporal and Spatial Settings in Swinburne

Using language to learn about documentsUsing Word Clouds for Topic Modeling Results

What are documents?

For this discussion, digital versions of works of spoken or written language

Examples: books, articles, transcripts, emails,

tweets…

Documents as context

Documents have:• form(at)• style• provenance• entities• intentions

STUDIES OF DOCUMENTS

Why study documents?

• Describe a corpus• Compare/organize documents• Locate relevant information/filter out

irrelevant information

Describing a corpus

• Finding regularities/differences across groups of documents

• Developing theories of structure, style, etc. that can then be tested or applied

• May be manual (content analysis) or computer-assisted (statistical)

Example: Storylines

http://xkcd.com/657/

Differences of format, genre, participants…

• Articles may have sections, but these will vary by discipline and type of article

• Books may be fiction or non-fiction (or both)

• Transcripts may refer to multiple speakers, non-text content

• …ad infinitum

Example: Literature Fingerprinting

Keim, D. A., & Oelke, D. (2007). Literature fingerprinting: A new method for visual literary analysis. In IEEE Symposium on Visual Analytics Science and Technology, VAST 2007 (pp.115-122). doi: 10.1109/VAST.2007.4389004

Organizing documents

Detect similarity between documents and a known category (or simply among themselves)

Supports browsing, sentiment analysis, authorship detection

Example: Bohemian Bookshelf

Thudt, A., Hinrichs, U., & Carpendale, S. (2012). The Bohemian Bookshelf: Supporting Serendipitous Book Discoveries through Information Visualization. In CHI '12: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, to appear.

Similarity based on…

• common document attributesauthorship, genre

• common language patternstopics, phrases

• common entity referencescharacters, citations

Example: Quantitative Formalism

Allison, S., Heuser, R., Jockers, M., Moretti, F., & Witmore, M. (2011). Quantitative formalism: An experiment. Pamphlets of the Stanford Literary Lab (vol. 1).

Example: Clinton’s DNC Speech

http://b.globe.com/TogUqq

Example: View DHQ

http://digitalliterature.net/viewDHQ/vis3.html

Classification

• assigning an object to a single class• often supervised, using an existing

classification scheme and a tagged corpus

Example: Relative signatures

Jankowska, M., Keselj, V., & Milios, E. (2012). Relative n-gram signatures: Document visualization at the level of character n-grams. In Proceedings of IEEE Conference on Visual Analytics Science and Technology 2012 (pp. 103-112).

Categorization

• assigning documents to one or more categories

• suggestive of unsupervised clustering techniques

• design choices made to fit particular tasks or goals

Example: UCSD Map of Science

Börner, K., Klavans, R., Patek, M., Zoss, A. M., Biberstine, J. R., Light, R. P., Larivière, V., & Boyack, K. W. (2012). Design and update of a classification system: The UCSD Map of Science . PLoS ONE, 7(7), e39464.

Example: NIH Map Viewer

https://app.nihmaps.org/nih/browser/

Reference systems, infrastructureWhat do we gain by adding structure?

What do we lose?

SUMMARIZING DOCUMENTS

Text is only one component of a document.

Research questions often push us to be creative with how we operationalize constructs.

The richness of language and documents is best preserved by using multiple, complementary approaches.

QUESTIONS?angela.zoss@duke.edu