Measuring praise and criticism: Inference of semantic orientation from association
ACM Transactions on Information Systems (TOIS)
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
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
Determining the semantic orientation of terms through gloss classification
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
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
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
NAACL-Short '07 Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics; Companion Volume, Short Papers
Pattern and keyword based opinion analysis from opinionated texts
Proceedings of the International Conference & Workshop on Emerging Trends in Technology
Sentiment dictionary for effective detection of web users' opinion
Proceedings of the International Conference on Advances in Computing, Communications and Informatics
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In this paper we present an unsupervised approach to identify opinion of web users using a set of significant sentences from an opinionated text and to classify web user's opinion into positive or negative. Web users document their opinion in opinionated sites, shopping sites, personal pages etc., to express and share their opinion with other web users. The opinion expressed by web users may be on diverse topics such as politics, sports, products, movies etc. These opinions will be very useful to others such as, leaders of political parties, selection committees of various sports, business analysts and other stake holders of products, directors and producers of movies as well as to the other concerned web users. We use an unsupervised semantic based approach to find users opinion. Our approach first detects subjective phrases and uses these phrases along with semantic orientation score to identify user's opinion from a set of empirically selected significant sentences. Our approach provides better results than the other approaches applied on different data sets.