Learning Subjective Adjectives from Corpora
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Performance Guarantees for Hierarchical Clustering
COLT '02 Proceedings of the 15th Annual Conference on Computational Learning Theory
Measuring praise and criticism: Inference of semantic orientation from association
ACM Transactions on Information Systems (TOIS)
Sentiment analysis: capturing favorability using natural language processing
Proceedings of the 2nd international conference on Knowledge capture
Predicting the semantic orientation of adjectives
ACL '98 Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics
Effects of adjective orientation and gradability on sentence subjectivity
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 1
Mining and summarizing customer reviews
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Accurate unlexicalized parsing
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Learning extraction patterns for subjective expressions
EMNLP '03 Proceedings of the 2003 conference on Empirical methods in natural language processing
EMNLP '03 Proceedings of the 2003 conference on Empirical methods in natural language processing
Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data (Data-Centric Systems and Applications)
Extracting semantic orientations of words using spin model
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Incorporating non-local information into information extraction systems by Gibbs sampling
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Determining the sentiment of opinions
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Extracting product features and opinions from reviews
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
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 shortest path dependency kernel for relation extraction
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
OpinionFinder: a system for subjectivity analysis
HLT-Demo '05 Proceedings of HLT/EMNLP on Interactive Demonstrations
Topic sentiment mixture: modeling facets and opinions in weblogs
Proceedings of the 16th international conference on World Wide Web
A tutorial on spectral clustering
Statistics and Computing
Introduction to Information Retrieval
Introduction to Information Retrieval
Opinion Mining and Sentiment Analysis
Foundations and Trends in Information Retrieval
BART: a modular toolkit for coreference resolution
HLT-Demonstrations '08 Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics on Human Language Technologies: Demo Session
Topic identification for fine-grained opinion analysis
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
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
Identifying text polarity using random walks
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Using anaphora resolution to improve opinion target identification in movie reviews
ACLShort '10 Proceedings of the ACL 2010 Conference Short Papers
Multi-level structured models for document-level sentiment classification
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
What's with the attitude?: identifying sentences with attitude in online discussions
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
Clairlib: a toolkit for natural language processing, information retrieval, and network analysis
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: Systems Demonstrations
Identifying the semantic orientation of foreign words
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers - Volume 2
Cats rule and dogs drool!: classifying stance in online debate
WASSA '11 Proceedings of the 2nd Workshop on Computational Approaches to Subjectivity and Sentiment Analysis
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Short Papers - Volume 2
Modeling interaction features for debate side clustering
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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The rapid and continuous growth of social networking sites has led to the emergence of many communities of communicating groups. Many of these groups discuss ideological and political topics. It is not uncommon that the participants in such discussions split into two or more subgroups. The members of each subgroup share the same opinion toward the discussion topic and are more likely to agree with members of the same subgroup and disagree with members from opposing subgroups. In this paper, we propose an unsupervised approach for automatically detecting discussant subgroups in online communities. We analyze the text exchanged between the participants of a discussion to identify the attitude they carry toward each other and towards the various aspects of the discussion topic. We use attitude predictions to construct an attitude vector for each discussant. We use clustering techniques to cluster these vectors and, hence, determine the subgroup membership of each participant. We compare our methods to text clustering and other baselines, and show that our method achieves promising results.