Recognizing Stances in Online Debates

This paper presents an unsupervised opinion analysis method for debate-side classification, i.e., recognizing which stance a person is taking in an online debate. In order to handle the complexities of this genre, we mine the web to learn associations that are indicative of opinion stances in debates. We combine this knowledge with discourse information, and formulate the debate side classification task as an Integer Linear Programming problem. Our results show that our method is substantially better than challenging baseline methods.

• Publication year

  2009

• Venue

  Annual Meeting of the Association for Computational Linguistics

• Publication date

  2009-08-02

• Fields of study

  Computer Science

• Identifiers
  DOI 10.3115/1687878.1687912
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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• Preface

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• Bibliography

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• Index

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• Introduction

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• Appendix

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