Computational Linguistics
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
A sentimental education: sentiment analysis using subjectivity summarization based on minimum cuts
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the 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
Opinion Mining and Sentiment Analysis
Foundations and Trends in Information Retrieval
Recognizing contextual polarity: An exploration of features for phrase-level sentiment analysis
Computational Linguistics
Exploiting subjectivity classification to improve information extraction
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 3
Identifying expressions of opinion in context
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Subjectivity word sense disambiguation
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1 - Volume 1
Generating focused topic-specific sentiment lexicons
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
A study of information retrieval weighting schemes for sentiment analysis
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Jointly modeling aspects and opinions with a MaxEnt-LDA hybrid
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
Chinese sentence-level sentiment classification based on fuzzy sets
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
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Lexical subjectivity strength has proven to be of great value to subjectivity classification. However, the quantitative calculation of lexical subjectivity strength has not yet been much explored. This paper presents a fuzzy set based approach to automatically learn lexical subjectivity strength for Chinese opinionated sentence identification. To approach this task, log-linear probabilities are employed to extract a set of subjective words from opinionated sentences, and three fuzzy sets, namely low-strength subjectivity, medium-strength subjectivity and high-strength subjectivity, are then defined to represent their respective classes of subjectivity strength. Furthermore, three membership functions are built to indicate the degrees of subjective words in different fuzzy sets. Finally, the acquired lexical subjective strength is further exploited to perform subjectivity classification. The experimental results on the NTCIR-7 MOAT data demonstrate that the introduction of lexical subjective strength is beneficial to subjectivity classification.