Sounding For Meaning: Using Theories Of Knowledge Representation To Analyze Aural Patterns In Texts

Computational literary analytics that include frequency trends and collocation, topic modeling, and network analysis have relied on rapid and large-scale analysis of the word or strings of words. This essay shows that there are many other features of literary texts by which humanists make meaning other than the word, such as prosody and sound, and how computational methods allow us to do what has historically been a more difficult method of analysis - trying to understand how literary texts make meaning with these features. This paper will discuss a case study that uses theories of knowledge representation and research on phonetic and prosodic symbolism to develop analytics and visualizations that help readers discover aural and prosodic patterns in literary texts. To this end, this paper has two parts: (I) We describe the theories of knowledge representation and research into phonetic and prosodic symbolism that underpin the logics and ontologies of aurality incorporated in our project. This basic theory of aurality is reflected in our use of OpenMary, a text-to-speech application tool for extracting aural features; in the "flow" we coordinated to pre-process texts in SEASR's Meandre, a data flow environment; in the instance-based predictive modeling procedure that we developed for the project; and in ProseVis, the reader interface that we created to allow readers to discover aural features across literary texts. And (II), we discuss readings of several works by Gertrude Stein (the portraits "Matisse" and "Picasso" and the prose poem Tender Buttons) that were facilitated by this work.