Mining and summarizing customer reviews
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Feature-rich part-of-speech tagging with a cyclic dependency network
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
Accurate unlexicalized parsing
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Recognizing contextual polarity in phrase-level sentiment analysis
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
A Joint Topic and Perspective Model for Ideological Discourse
ECML PKDD '08 Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases - Part II
More than words: syntactic packaging and implicit sentiment
NAACL '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Generalizing dependency features for opinion mining
ACLShort '09 Proceedings of the ACL-IJCNLP 2009 Conference Short Papers
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
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
Summarizing contrastive viewpoints in opinionated text
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
How can you say such things?!?: recognizing disagreement in informal political argument
LSM '11 Proceedings of the Workshop on Languages in Social Media
Mining contrastive opinions on political texts using cross-perspective topic model
Proceedings of the fifth ACM international conference on Web search and data mining
Mining contentions from discussions and debates
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Subgroup detector: a system for detecting subgroups in online discussions
ACL '12 Proceedings of the ACL 2012 System Demonstrations
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers - Volume 1
Subgroup detection in ideological discussions
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers - Volume 1
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Short Papers - Volume 2
Combining textual entailment and argumentation theory for supporting online debates interactions
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Short Papers - Volume 2
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
Unsupervised discovery of opposing opinion networks from forum discussions
Proceedings of the 21st ACM international conference on Information and knowledge management
PolariCQ: polarity classification of political quotations
Proceedings of the 21st ACM international conference on Information and knowledge management
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Online discussion forums are popular social media platforms for users to express their opinions and discuss controversial issues with each other. To automatically identify the sides/stances of posts or users from textual content in forums is an important task to help mine online opinions. To tackle the task, it is important to exploit user posts that implicitly contain support and dispute (interaction) information. The challenge we face is how to mine such interaction information from the content of posts and how to use them to help identify stances. This paper proposes a two-stage solution based on latent variable models: an interaction feature identification stage to mine interaction features from structured debate posts with known sides and reply intentions; and a clustering stage to incorporate interaction features and model the interplay between interactions and sides for debate side clustering. Empirical evaluation shows that the learned interaction features provide good insights into user interactions and that with these features our debate side model shows significant improvement over other baseline methods.