Mining newsgroups using networks arising from social behavior
WWW '03 Proceedings of the 12th international conference on World Wide Web
Determining the Polarity and Source of Opinions Expressed in Political Debates
CICLing '09 Proceedings of the 10th International Conference on Computational Linguistics and Intelligent Text Processing
Get out the vote: determining support or opposition from congressional floor-debate transcripts
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
Recognizing stances in online debates
ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 1 - Volume 1
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Recognizing stances in ideological on-line debates
CAAGET '10 Proceedings of the NAACL HLT 2010 Workshop on Computational Approaches to Analysis and Generation of Emotion in Text
Multi-level structured models for document-level sentiment classification
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
Collective classification of congressional floor-debate transcripts
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Identifying Justifications in Written Dialogs
ICSC '11 Proceedings of the 2011 IEEE Fifth International Conference on Semantic Computing
Computer Speech and Language
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Public debate functions as a forum for both expressing and forming opinions, an important aspect of public life. We present results for automatically classifying posts in online debate as to the position, or stance that the speaker takes on an issue, such as Pro or Con. We show that representing the dialogic structure of the debates in terms of agreement relations between speakers, greatly improves performance for stance classification, over models that operate on post content and parent-post context alone.