ABSTRACT
This paper presents a new approach to phrase-level sentiment analysis that first determines whether an expression is neutral or polar and then disambiguates the polarity of the polar expressions. With this approach, the system is able to automatically identify the contextual polarity for a large subset of sentiment expressions, achieving results that are significantly better than baseline.
- P. Beineke, T. Hastie, and S. Vaithyanathan. 2004. The sentimental factor: Improving review classification via human-provided information. In ACL-2004. Google ScholarDigital Library
- M. Collins. 1997. Three generative, lexicalised models for statistical parsing. In ACL-1997. Google ScholarDigital Library
- K. Dave, S. Lawrence, and D. M. Pennock. 2003. Mining the peanut gallery: Opinion extraction and semantic classification of product reviews. In WWW-2003. The General-Inquirer. 2000. http://www.wjh.harvard.edu/~inquirer/spreadsheet_guide.htm. Google ScholarDigital Library
- G. Grefenstette, Y. Qu, J. G. Shanahan, and D. A. Evans. 2001. Coupling niche browsers and affect analysis for an opinion mining application. In RIAO-2004.Google Scholar
- V. Hatzivassiloglou and K. McKeown. 1997. Predicting the semantic orientation of adjectives. In ACL-1997. Google ScholarDigital Library
- M. Hu and B. Liu. 2004. Mining and summarizing customer reviews. In KDD-2004. Google ScholarDigital Library
- J. Kamps and M. Marx. 2002. Words with attitude. In 1st International WordNet Conference.Google Scholar
- S-M. Kim and E. Hovy. 2004. Determining the sentiment of opinions. In Coling 2004. Google ScholarDigital Library
- T. Nasukawa and J. Yi. 2003. Sentiment analysis: Capturing favorability using natural language processing. In K-CAP 2003. Google ScholarDigital Library
- B. Pang and L. Lee. 2004. A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts. In ACL-2004. Google ScholarDigital Library
- L. Polanya and A. Zaenen. 2004. Contextual valence shifters. In Working Notes --- Exploring Attitude and Affect in Text (AAAI Spring Symposium Series).Google Scholar
- R. Quirk, S. Greenbaum, G. Leech, and J. Svartvik. 1985. A Comprehensive Grammar of the English Language. Longman, New York.Google Scholar
- E. Riloff and J. Wiebe. 2003. Learning extraction patterns for subjective expressions. In EMNLP-2003. Google ScholarDigital Library
- R. E. Schapire and Y. Singer. 2000. BoosTexter: A boosting-based system for text categorization. Machine Learning, 39(2/3): 135--168. Google ScholarDigital Library
- P. Turney. 2002. Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews. In ACL-2002. Google ScholarDigital Library
- J. Wiebe and E. Riloff. 2005. Creating subjective and objective sentence classifiers from unannotated texts. In CICLing-2005.Google Scholar
- J. Wiebe, T. Wilson, and C. Cardie. 2005. Annotating expressions of opinions and emotions in language. Language Resources and Evalution (formerly Computers and the Humanities), 1(2).Google Scholar
- F. Xia and M. Palmer. 2001. Converting dependency structures to phrase structures. In HLT-2001. Google ScholarDigital Library
- J. Yi, T. Nasukawa, R. Bunescu, and W. Niblack. 2003. Sentiment analyzer: Extracting sentiments about a given topic using natural language processing techniques. In IEEE ICDM-2003. Google ScholarDigital Library
- H. Yu and V. Hatzivassiloglou. 2003. Towards answering opinion questions: Separating facts from opinions and identifying the polarity of opinion sentences. In EMNLP-2003. Google ScholarDigital Library
- Recognizing contextual polarity in phrase-level sentiment analysis
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