Speech act profiling: A probabilistic method for analyzing persistent conversations and their participants

Douglas P. Twitchell, Jay F. Nunamaker

Research output: Contribution to journalConference articlepeer-review

18 Scopus citations

Abstract

The increase in persistent conversations in the form of chat and instant messaging (IM) has presented new opportunities for researchers. This paper describes a method for evaluating and visualizing persistent conversations by creating a speech act profile for conversation participants using speech act theory and concepts from fuzzy logic. This method can be used either to score a participant based on possible intentions or to create a visual map of those intentions. Transcripts from the Switchboard corpus, which have been marked up with speech act labels according to a SWBD-DAMSL tag set of 42 tags, are used to train language models and a modified hidden Markov model (HMM) to obtain probabilities for each speech act type for a given sentence. Rather than choosing the speech act with the maximum probability and assigning it to the sentence, the probabilities are aggregated for each conversation participant creating a set of speech act profiles, which can be visualized as a radar graphs. Several example profiles are shown along with possible interpretations. The profiles can be used as an overall picture of a conversation, and may be useful in various analyses of persistent conversations including information retrieval, deception detection, and online technical support monitoring.

Original languageEnglish (US)
Article numberDDPCN06
Pages (from-to)1713-1722
Number of pages10
JournalProceedings of the Hawaii International Conference on System Sciences
Volume37
StatePublished - Dec 1 2004
EventProceedings of the Hawaii International Conference on System Sciences - Big Island, HI., United States
Duration: Jan 5 2004Jan 8 2004

ASJC Scopus subject areas

  • Computer Science(all)

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