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Mining the peanut gallery: opinion extraction and semantic classification of product reviews
Abstract
The web contains a wealth of product reviews, but sifting through them is a daunting task. Ideally, an opinion mining tool would process a set of search results for a given item, generating a list of product attributes (quality, features, etc.) and aggregating opinions about each of them (poor, mixed, good). We begin by identifying the unique properties of this problem and develop a method for automatically distinguishing between positive and negative reviews. Our classifier draws on information retrieval techniques for feature extraction and scoring, and the results for various metrics and heuristics vary depending on the testing situation. The best methods work as well as or better than traditional machine learning. When operating on individual sentences collected from web searches, performance is limited due to noise and ambiguity. But in the context of a complete web-based tool and aided by a simple method for grouping sentences into attributes, the results are qualitatively quite useful.
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Cited by
Mining and summarizing customer reviews
Hu, M., & Liu, B. (2004) · Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/1014052.1014073 · Cited by 7,793
Lexicon-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
Jindal, N., & Liu, B. (2008) · Proceedings of the international conference on Web search and web data mining - WSDM '08 · doi:10.1145/1341531.1341560 · Cited by 1,542
Movie review mining and summarization
Zhuang, L., Jing, F., & Zhu, X.-Y. (2006) · Proceedings of the 15th ACM international conference on Information and knowledge management - CIKM '06 · doi:10.1145/1183614.1183625 · Cited by 894
Sentiment analysis: A combined approach
Prabowo, R., & Thelwall, M. (2009) · Journal of Informetrics · doi:10.1016/j.joi.2009.01.003 · Cited by 803
Metadata
- Authors
- Dave, K., Lawrence, S., & Pennock, D. M.
- Venue
- Proceedings of the twelfth international conference on World Wide Web - WWW '03
- Publisher
- ACM Press
- Published
- 2003
- Pages
- 519
- OpenAlex
- W2115023510
- Cited by
- 1,916
- References
- 0
- Type
- conference abstract
- Language
- en
- Open access
- closed
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- Dave, K., Lawrence, S., & Pennock, D. M. (2003). Mining the peanut gallery: opinion extraction and semantic classification of product reviews. Proceedings of the twelfth international conference on World Wide Web - WWW '03. https://doi.org/10.1145/775152.775226
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