Verbatim text transcripts capture the rapid exchange of opinions, arguments, and information among participants of a conversation. As a form of communication that is based on social interaction, multiparty conversations are characterized by an incremental development of their content structure. In contrast to highly-edited text data (e.g., literary, scientific, and technical publications), verbatim text transcripts contain non-standard lexical items and syntactic patterns. Thus, analyzing these transcripts automatically introduces multiple challenges.
In this talk, I will present approaches developed (in context of the VisArgue project) to enable humanities and social science scholars to get different perspectives on verbatim text data in order to capture strategies of successful rhetoric and argumentation. To analyze why specific discourse patterns occur in a transcript, three main pillars of communication are studied through answering the following questions: (1) What is being said? (2) How is it being said? (3) By whom is it being said?
In addition to reporting on visualization techniques for the analysis of conversation dynamics, I will argue for the importance of tuning automatic content analysis models to unique textual characteristics, appearing, for example, in verbatim text transcripts. In particular, I will present a visual analytics framework for the progressive learning of topic modeling parameters. Our human-in-the-loop process simplifies the model tuning task through intuitive user feedback on the relationship between topics and documents.
Lecture Slides will appear here soon.
Video footage will be added in the future.