Thumbs up or thumbs down?: semantic orientation applied to unsupervised classification of reviews
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Using appraisal groups for sentiment analysis
Proceedings of the 14th ACM international conference on Information and knowledge management
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
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Meaningful clustering of senses helps boost word sense disambiguation performance
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the 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
Determining the sentiment of opinions
COLING '04 Proceedings of the 20th international conference on 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
Fine-grained subjectivity and sentiment analysis: recognizing the intensity, polarity, and attitudes of private states
From words to senses: a case study of subjectivity recognition
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
Contextual phrase-level polarity analysis using lexical affect scoring and syntactic N-grams
EACL '09 Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics
NAACL-Short '06 Proceedings of the Human Language Technology Conference of the NAACL, Companion Volume: Short Papers
Subjectivity recognition on word senses via semi-supervised mincuts
NAACL '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Integrating knowledge for subjectivity sense labeling
NAACL '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Subjectivity word sense disambiguation
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1 - Volume 1
Word sense subjectivity for cross-lingual lexical substitution
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Amazon Mechanical Turk for subjectivity word sense disambiguation
CSLDAMT '10 Proceedings of the NAACL HLT 2010 Workshop on Creating Speech and Language Data with Amazon's Mechanical Turk
Sentimantics: conceptual spaces for lexical sentiment polarity representation with contextuality
WASSA '12 Proceedings of the 3rd Workshop in Computational Approaches to Subjectivity and Sentiment Analysis
An enhanced semantic tree kernel for sentiment polarity classification
CICLing'13 Proceedings of the 14th international conference on Computational Linguistics and Intelligent Text Processing - Volume 2
Factored semantic sequence kernel for sentiment polarity classification
SLSP'13 Proceedings of the First international conference on Statistical Language and Speech Processing
Bootstrapping polarity classifiers with rule-based classification
Language Resources and Evaluation
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Subjectivity word sense disambiguation (SWSD) is automatically determining which word instances in a corpus are being used with subjective senses, and which are being used with objective senses. SWSD has been shown to improve the performance of contextual opinion analysis, but only on a small scale and using manually developed integration rules. In this paper, we scale up the integration of SWSD into contextual opinion analysis and still obtain improvements in performance, by successfully gathering data annotated by non-expert annotators. Further, by improving the method for integrating SWSD into contextual opinion analysis, even greater benefits from SWSD are achieved than in previous work. We thus more firmly demonstrate the potential of SWSD to improve contextual opinion analysis.