A tool for exploring texts on non-word basis.
Or in the words of the project:
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 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.
Textual analysis tools are “smoking gun” detectors.
CEO is unlikely to make inappropriate comments in a spreadsheet or data feed. Emails on the other hand… 😉
Big or little data, the goal is to have the “right” data.