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Towards answering opinion questions: separating facts from opinions and identifying the polarity of opinion sentences

Authors:

Hong Yu,

Vasileios HatzivassiloglouAuthors Info & Claims

EMNLP '03: Proceedings of the 2003 conference on Empirical methods in natural language processing

Pages 129 - 136

https://doi.org/10.3115/1119355.1119372

Published: 11 July 2003 Publication History

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Abstract

Opinion question answering is a challenging task for natural language processing. In this paper, we discuss a necessary component for an opinion question answering system: separating opinions from fact, at both the document and sentence level. We present a Bayesian classifier for discriminating between documents with a preponderance of opinions such as editorials from regular news stories, and describe three unsupervised, statistical techniques for the significantly harder task of detecting opinions at the sentence level. We also present a first model for classifying opinion sentences as positive or negative in terms of the main perspective being expressed in the opinion. Results from a large collection of news stories and a human evaluation of 400 sentences are reported, indicating that we achieve very high performance in document classification (upwards of 97% precision and recall), and respectable performance in detecting opinions and classifying them at the sentence level as positive, negative, or neutral (up to 91% accuracy).

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Cited By

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Singh UAbhishek KAzad H(2024)A Survey of Cutting-edge Multimodal Sentiment AnalysisACM Computing Surveys10.1145/365214956:9(1-38)Online publication date: 25-Apr-2024
https://dl.acm.org/doi/10.1145/3652149
Keith Norambuena BMitra TNorth C(2023)A Survey on Event-Based News Narrative ExtractionACM Computing Surveys10.1145/358474155:14s(1-39)Online publication date: 17-Jul-2023
https://dl.acm.org/doi/10.1145/3584741
Liang CLiao B(2021)Online Boycott through A Sentiment Analysis: A Pilot StudyProceedings of the 8th Multidisciplinary International Social Networks Conference10.1145/3504006.3504026(91-94)Online publication date: 15-Nov-2021
https://dl.acm.org/doi/10.1145/3504006.3504026
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EMNLP '03: Proceedings of the 2003 conference on Empirical methods in natural language processing

July 2003

224 pages

Program Chairs:
Michael Collins
MIT AI Lab
,
Mark Steedman
University of Edinburgh

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Association for Computational Linguistics

United States

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Published: 11 July 2003

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Overall Acceptance Rate 73 of 234 submissions, 31%

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Singh UAbhishek KAzad H(2024)A Survey of Cutting-edge Multimodal Sentiment AnalysisACM Computing Surveys10.1145/365214956:9(1-38)Online publication date: 25-Apr-2024
https://dl.acm.org/doi/10.1145/3652149
Keith Norambuena BMitra TNorth C(2023)A Survey on Event-Based News Narrative ExtractionACM Computing Surveys10.1145/358474155:14s(1-39)Online publication date: 17-Jul-2023
https://dl.acm.org/doi/10.1145/3584741
Liang CLiao B(2021)Online Boycott through A Sentiment Analysis: A Pilot StudyProceedings of the 8th Multidisciplinary International Social Networks Conference10.1145/3504006.3504026(91-94)Online publication date: 15-Nov-2021
https://dl.acm.org/doi/10.1145/3504006.3504026
Manga EKaranikolas NMarinagi CSkourlas C(2021)Evaluating Citizen Comments in Public Consultations Using Data MiningProceedings of the 25th Pan-Hellenic Conference on Informatics10.1145/3503823.3503902(430-435)Online publication date: 26-Nov-2021
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Chang JCheng JDanescu-Niculescu-Mizil C(2020)Don’t Let Me Be Misunderstood:Comparing Intentions and Perceptions in Online DiscussionsProceedings of The Web Conference 202010.1145/3366423.3380273(2066-2077)Online publication date: 20-Apr-2020
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Desai URamasamy VKiper JZhang JSherriff MHeckman SCutter PMonge A(2020)A Study on Student Performance Evaluation using Discussion Board NetworksProceedings of the 51st ACM Technical Symposium on Computer Science Education10.1145/3328778.3366876(500-506)Online publication date: 26-Feb-2020
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Smarandache FColhon MVlăduţescu ŞNegrea X(2019)Word-level neutrosophic sentiment similarityApplied Soft Computing10.1016/j.asoc.2019.03.03480:C(167-176)Online publication date: 1-Jul-2019
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Tian FWu FFei XShah NZheng QWang Y(2019)Improving generalization ability of instance transfer-based imbalanced sentiment classification of turn-level interactive Chinese textsService Oriented Computing and Applications10.1007/s11761-019-00264-y13:2(155-167)Online publication date: 1-Jun-2019
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Answering opinion questions with random walks on graphs

Opinion summarization: automatically creating useful representations of the opinions expressed in text

Identifying Opinion Sentences and Opinion Holders in Internet Public Opinion

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