Foundations of statistical natural language processing
Foundations of statistical natural language processing
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Learning Subjective Adjectives from Corpora
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Effects of adjective orientation and gradability on sentence subjectivity
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 1
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
Thumbs up?: sentiment classification using machine learning techniques
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
Learning subjective nouns using extraction pattern bootstrapping
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
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
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
A holistic lexicon-based approach to opinion mining
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
Opinion Mining and Sentiment Analysis
Foundations and Trends in Information Retrieval
Yahoo! for Amazon: Sentiment Extraction from Small Talk on the Web
Management Science
Mining opinion features in customer reviews
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Discovering content-based behavioral roles in social networks
Decision Support Systems
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Recent solutions for sentiment analysis have relied on feature selection methods ranging from lexicon-based approaches where the set of features are generated by humans, to approaches that use general statistical measures where features are selected solely on empirical evidence. The advantage of statistical approaches is that they are fully automatic, however, they often fail to separate features that carry sentiment from those that do not. In this paper we propose a set of new feature selection schemes that use a Content and Syntax model to automatically learn a set of features in a review document by separating the entities that are being reviewed from the subjective expressions that describe those entities in terms of polarities. By focusing only on the subjective expressions and ignoring the entities, we can choose more salient features for document-level sentiment analysis. The results obtained from using these features in a maximum entropy classifier are competitive with the state-of-the-art machine learning approaches.