ProseVis is a visualization tool developed as part of a use case supported by the Andrew W. Mellon Foundation through a grant titled "SEASR Services," in which we seek to identify other features than the "word" to analyze texts. These features comprise sound including parts-of-speech, accent, phoneme, stress, tone, break index.
ProseVis been awarded "Best Infovis" in the 2012 Digital Humanities Awards as part of A Thousand Words Project at the Texas Advanced Computing Center.
ProseVis allows a reader to map the features extracted from OpenMary (http://mary.dfki.de/) Text-to-speech System and predictive classification data to the "original" text. We developed this project with the ultimate goal of facilitating a reader's ability to analyze and disseminate the results in human readable form. Research has shown that mapping the data to the text in its original form allows for the kind of human reading that literary scholars engage: words in the context of phrases, sentences, lines, stanzas, and paragraphs (Clement 2008). Recreating the context of the page not only allows for the simultaneous consideration of multiple representations of knowledge or readings (since every reader's perspective on the context will be different) but it also allows for a more transparent view of the underlying data. If a human can see the data (the syllables, the sounds, the parts-of-speech) within the context in which they are used to reading, with the data mapped back onto the full text, then the reader is empowered within this familiar context to read what might otherwise be an unfamiliar representation tabular representation of the text. For these reasons, we developed ProseVis as a reader interface to allow scholars to work with the data in a language or context in which we are used to saying things about the world.
Clement, T., Tcheng, D., Auvil, L., Capitanu, B., and Barbosa, J. "Distant Listening to Gertrude Stein's 'Melanctha': Using Similarity Analysis in a Discovery Paradigm to Analyze Prosody and Author Influence." Literary and Linguistic Computing 28.4 (2013): 582-602.
Clement, T. "Distant Listening or Playing Visualizations Pleasantly with the Eyes and Ears." Digital Studies / Le champ numérique. 3.2 (2012).
Clement, T., Tcheng, D., Auvil, L., Capitanu, B. Monroe, M. "Sounding for Meaning: Using Theories of Knowledge Representation to Analyze Aural Patterns in Texts." digital humanities quarterly. 7.1 (2013).
Clement, T. "Methodologies in the Digital Humanities for Analyzing Aural Patterns in Texts." Proceedings of the 2012 iConference. New York, NY, USA: ACM, 2012. 287Š293. Web. 21 Feb. 2012.
"Sounding it Out: Modeling Aurality for Large-Scale Text Collection Analysis." Digital Humanities Conference, University of Hamburg, Germany (July 2012).
Clement, T. "Sounding it Out: Modeling Aurality for Large-Scale Text Collection Analysis." Big Data: Text Mining in the Digital Humanities, McGill University (May 2012).
Clement, T. "Sounding for Meaning: Knowledge Representation and Methodologies in the Digital Humanities for Analyzing Aural Textual Patterns." Critical Digital Humanities Research Group, UC Riverside (April 2012).
"Circles: Sounding SteinÕs Texts by Using Digital Tools for Distant Listening." Modern Language Association (MLA) Conference, Seattle (January 2012).
"Sounding it Out: Modeling Aurality for Large-scale Text Collection Analysis." Modern Language Association (MLA) Conference, Seattle (January 2012).
Clement, T. "Sounding it Out: Modeling Aurality for Large-scale Text Collection Analysis." New York Public Library (November 2011).
"Sounding it out: modeling orality for large-scale text collection analysis." Representing Knowledge Conference, University of Kansas (September 2011).
Funded by The Andrew W. Mellon Foundation.