Text Categorization Based on Regularized Linear Classification Methods
Information Retrieval
Mining newsgroups using networks arising from social behavior
WWW '03 Proceedings of the 12th international conference on World Wide Web
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Trust Region Newton Method for Logistic Regression
The Journal of Machine Learning Research
LIBLINEAR: A Library for Large Linear Classification
The Journal of Machine Learning Research
AVA: Adjective-Verb-Adverb Combinations for Sentiment Analysis
IEEE Intelligent Systems
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
Cross-domain sentiment classification via spectral feature alignment
Proceedings of the 19th international conference on World wide web
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
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Detection of agreement and disagreement in broadcast conversations
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers - Volume 2
What pushes their buttons?: predicting comment polarity from the content of political blog posts
LSM '11 Proceedings of the Workshop on Languages in Social Media
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Online debate forums provide a powerful communication platform for individual users to share information, exchange ideas and express opinions on a variety of topics. Understanding people's opinions in such forums is an important task as its results can be used in many ways. It is, however, a challenging task because of the informal language use and the dynamic nature of online conversations. In this paper, we propose a new method for identifying participants' agreement or disagreement on an issue by exploiting information contained in each of the posts. Our proposed method first regards each post in its local context, then aggregates posts to estimate a participant's overall position. We have explored the use of sentiment, emotional and durational features to improve the accuracy of automatic agreement and disagreement classification. Our experimental results have shown that aggregating local positions over posts yields better performance than non-aggregation baselines when identifying users' global positions on an issue.