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Cited by 1,698

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Extracting product features and opinions from reviews

Open access
Popescu, Ana-Maria, Etzioni, Oren·2005·Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing - HLT '05
DOI 10.3115/1220575.1220618
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Abstract

Consumers are often forced to wade through many on-line reviews in order to make an informed product choice. This paper introduces Opine, an unsupervised information-extraction system which mines reviews in order to build a model of important product features, their evaluation by reviewers, and their relative quality across products.Compared to previous work, Opine achieves 22% higher precision (with only 3% lower recall) on the feature extraction task. Opine's novel use of relaxation labeling for finding the semantic orientation of words in context leads to strong performance on the tasks of finding opinion phrases and their polarity.

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Metadata

Authors
Popescu, A.-M., & Etzioni, O.
Venue
Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing - HLT '05
Publisher
Association for Computational Linguistics
Published
2005
Pages
339-346
DOI
10.3115/1220575.1220618
OpenAlex
W2081375810
Cited by
1,698
References
0
FWCI
33.39
Type
conference paper
Language
en
Open access
gold

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CoChat. (2026). Citation check: Extracting product features and opinions from reviews. https://app.cochat.ai/papers/10.3115/1220575.1220618
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Popescu, A.-M., & Etzioni, O. (2005). Extracting product features and opinions from reviews. Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing - HLT '05. https://doi.org/10.3115/1220575.1220618

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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.