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Recognizing contextual polarity in phrase-level sentiment analysis
Open accessAbstract
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.
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Citations
Cited by
Twitter mood predicts the stock market
Bollen, J., Mao, H., & Zeng, X. (2011) · Journal of Computational Science · doi:10.1016/j.jocs.2010.12.007 · Cited by 5,206
“We adopt OF’s subjective lexicon that has been established upon previous work [37], [38], [24].”
MethodLexicon-based methods for sentiment analysis
Taboada, M., Brooke, J., Tofiloski, M., Voll, K., & Stede, M. (2011) · Computational Linguistics · doi:10.1162/coli_a_00049 · Cited by 3,301
From Tweets to Polls: Linking Text Sentiment to Public Opinion Time Series
O'Connor, B., Balasubramanyan, R., Routledge, B., & Smith, N. (2010) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v4i1.14031 · Cited by 1,923
A review of affective computing: From unimodal analysis to multimodal fusion
Poria, S., Cambria, E., Bajpai, R., & Hussain, A. (2017) · Information Fusion · doi:10.1016/j.inffus.2017.02.003 · Cited by 1,644
Techniques and applications for sentiment analysis
Feldman, R. (2013) · Communications of the ACM · doi:10.1145/2436256.2436274 · Cited by 1,489
Metadata
- Authors
- Wilson, T., Wiebe, J., & Hoffmann, P.
- Venue
- Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing - HLT '05
- Published
- 2005
- Pages
- 347-354
- OpenAlex
- W2022204871
- Cited by
- 3,345
- References
- 0
- FWCI
- 30.77
- Type
- conference paper
- Language
- en
- Open access
- gold
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- Wilson, T., Wiebe, J., & Hoffmann, P. (2005). Recognizing contextual polarity in phrase-level sentiment analysis. Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing - HLT '05. https://doi.org/10.3115/1220575.1220619
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
