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http://hdl.handle.net/1903/7293
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| Title: | Spin: Lexical Semantics, Transitivity, and the Identification of Implicit Sentiment |
| Authors: | Greene, Stephan Charles |
| Advisors: | Resnik, Philip |
| Department/Program: | Linguistics |
| Type: | Dissertation |
| Sponsors: | Digital Repository at the University of Maryland
University of Maryland (College Park, Md.) |
| Keywords: | 0290
Language, Linguistics
0984
Computer Science
0800
Artificial Intelligence
sentiment analysis spin lexical semantics transitivity |
| Issue Date: | 1-Aug-2007 |
| Abstract: | Current interest in automatic sentiment analysis is motivated by a variety of information requirements. The vast majority of work in sentiment analysis has been specifically targeted at detecting subjective statements and mining opinions. This dissertation focuses on a different but related problem that to date has received relatively little attention in NLP research: detecting implicit sentiment, or spin, in text. This text classification task is distinguished from other sentiment analysis work in that there is no assumption that the documents to be classified with respect to sentiment are necessarily overt expressions of opinion. They rather are documents that might reveal a perspective. This dissertation describes a novel approach to the identification of implicit sentiment, motivated by ideas drawn from the literature on lexical semantics and argument structure, supported and refined through psycholinguistic experimentation. A relationship predictive of sentiment is established for... |
| URI: | http://hdl.handle.net/1903/7293 |
| Appears in Collections: | Linguistics Theses and Dissertations
UM Theses and Dissertations
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Size | Format | No. of Downloads |
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| umi-umd-4694.pdf | | 1391Kb | Adobe PDF | 255 | View/Open |
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