2026-09-19T07:38:39Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/854492026-06-06T00:49:26Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Catherine Havasi.
Muralidhar, Anjali
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2014-03-06T15:42:48Z
2014-03-06T15:42:48Z
2013
2013
http://hdl.handle.net/1721.1/85449
870967126
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2013.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 52-53).
The field of natural language processing has had success in analyzing sentiment and topics on written text, but similar analysis on dialogue is more difficult due to the fragmented and informal nature of speech. This work explores sentiment and topic analysis on data from the Switchboard dialogue corpus, as well as a dataset of recorded dialogues between parents and children while reading an interactive e-book. The goal was to be able to identify the emotion and mood of the dialogue in order to make inferences about what parents and children generally talk about when reading the book because conversations between an adult and child while reading a book can greatly contribute to the learning and development of young children.
by Anjali Muralidhar.
M.Eng.
53 pages
eng
Massachusetts Institute of Technology
M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.
http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
Understanding dialogue: sentiment and topic analysis of dialogue transcripts
Sentiment and topic analysis of dialogue transcripts
Thesis
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Publication
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<Abstract>The field of natural language processing has had success in analyzing sentiment and topics on written text, but similar analysis on dialogue is more difficult due to the fragmented and informal nature of speech. This work explores sentiment and topic analysis on data from the Switchboard dialogue corpus, as well as a dataset of recorded dialogues between parents and children while reading an interactive e-book. The goal was to be able to identify the emotion and mood of the dialogue in order to make inferences about what parents and children generally talk about when reading the book because conversations between an adult and child while reading a book can greatly contribute to the learning and development of young children.</Abstract>
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