Publication:
Machine learning techniques for persuasion dectection in conversation

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Authors
Ortiz, Pedro.
Subjects
Advisors
Martell, Craig H.
Date of Issue
2010-06
Date
Publisher
Monterey, California. Naval Postgraduate School
Language
Abstract
We determined that it is possible to automatically detect persuasion in conversations using three traditional machine learning techniques, naive bayes, maximum entropy, and support vector machine. These results are the first of their kind and serve as a baseline for all future work in this field. The three techniques consistently outperformed the baseline F-score, but not at a level that would be useful for real world applications. The corpus of data was comprised of four types of negotiation transcripts, labeled according to a persuasion model developed by James Cialdini. We discovered that the transcripts from the Davidian standoff in Waco, Texas were significantly different from the rest of the corpus. We have included suggestions for future work in the areas of data set improvements, feature set improvements, and additional research. Advancements in this field will contribute to the Global War on Terror by alerting intelligence analysts to enemy persuasion attempts and by enabling U.S. forces to conduct more effective information and psychological operations using local persuasion models.
Type
Thesis
Description
Department
Computer Science
Other Units
Naval Postgraduate School (U.S.)
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NPS Report Number
Sponsors
Funder
Format
xix, 109 p. : ill. ;
Citation
Distribution Statement
Approved for public release; distribution is unlimited.
Rights
This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.
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