In this paper we present an approach to identify opinion of web users from an opinionated text and to classify web user’s opinion into positive or negative. Web users document their opinion at 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 stakeholders of products, directors and producers of movies as well as to the other concerned web users. Today web users express their opinion using opinion elements such as opinion phrases, emoticons, short words etc. These form of opinion expressions are very popular and are used by a large number of web users to document their opinion. In this paper we use semantic based approach to find users opinion from opinionated phrases and emoticons. Our approach detects opinionated phrases and emoticons and uses them to obtain semantic orientation scores. These scores are later used to identify users opinion from opinionated texts. Our approach is effective and provides better results on different data sets.

1. Andrea, Esuli, Fabrizio, Sebastiani: Determining term subjectivity and term orientation for opinion mining. In: Proceedings of 11th Conference of the European Chapter of the Association for Computational Linguistics. Trento, Italy(2006)
2. Andrea, Esuli, Fabrizio, Sebastiani: Determining the semantic orientation of terms through gloss classification. In: Proceedings of 14th ACM International Conference on Information and Knowledge Management, pp. 617-624. Bremen, Germany(2005)
3. Alistair, Kennedy, Diana, Inkpen: Sentiment Classification of Movie and Product Reviews Using Contextual Valence Shifters. In: Proceedings of FINEXIN 2005, Workshop on the Analysis of Informal and Formal Information Exchange during Negotiations. canada( 2005)
4. Anil, kumar, K.M, Suresha: Identifying Subjective Phrases From Opinionated Texts Using Sentiment Product Lexicon, International Journal of Advanced Engineering & Applications. 2(263-271), (2010)
5. Anil, Kumar, K.M, Suresha: Detection of Neutral Phrases and Polarity Shifting of Few Phrases for Effective Classification of Opinionated Texts, International Journal of Computational Intelligence Research. 6(1) 43-58, (2010)
6. Bing, Liu, Minqing, Hu, Junsheng, Cheng: Opinion Observer: Analyzing and Comparing Opinions on the Web. Chiba, Japan(2005)
7. Bo, Pang, Lillian, Lee: A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts. In: Proceedings of 42nd Meeting of the Association for Computational Linguistics, pp. 271-278. Barcelona, Spain(2004)
8. Bo, Pang, Lillian, Lee, Shivakumar, Vaithyanathan: Thumbs up? Sentiment classification using machine learning techniques. In: Proceedings of 7th Conference on Empirical Methods in Natural Language Processing, pp. 79-86. Philadelphia, US(2002)
9. http://www.reviewcentre.com/
10. http://tartarus.org/ martin/PorterStemmer
11. Hugo:MontyLingua: An end-to-end natural language processor with common sense. (2003)
12. Jaap, Kamps, Maarten, Marx, Robert, J., Mokken, Maarten, De, Rijke:Using word-net to measure seman-tic orientation of adjectives. In: Proceedings of 4th Inter-national Conference on Language Resources and Evaluation, pp. 1115-1118. Lisbon, Portugal(2004)
13. Maite, Taboada, Jack, Grieve:Analyzing appraisal automatically. In: Proceedings of the AAAI Symposium on Exploring Attitude and Affect in Text: Theories and Applications. (2004)
14. Peter, D, Turney: Thumbs up or thumbs down? Se-mantic orientation applied to unsupervised classification of reviews. In: Proceedings of 40th Annual Meeting of the association for Computational Linguistics, pp. 417-424. Philadelphia (2002)
15. Peter, D, Turney, Michael, L, Littman: Measuring praise and criticism: Inference of semantic orientation from association, ACM Transactions on Information Systems, pp. 315-346. (2003)
16. Sara, Owsley, Sanjay, Sood, Kristian, J, Hammond: Domain specific affective classification of document. In: Proceedings of AAAI-2006 Spring Symposium on Computational Approaches to Analyzing Weblogs. California, US (2006)
17. Stone, P.J: Thematic text analysis: New agendas for analyzing text content. In C. Roberts (Ed.), Text Analysis for the Social Sciences, Mahwah. Erlbaum (1997)
18. Theresa, Wilson, Janyce, Wiebe, Paul, Hoffmann: Recognizing Contextual Polarity in Phrase-Level Sentiment Analysis. In: Proceedings of HLT/EMNLP, Vancouver, Canada, (2005)
19. Vasileios, Hatzivassiloglou, Kathleen, R., McKeown: Predicting the semantic orientation of adjectives. In: Proceedings of 35th Annual Meeting of the Association for Computational Linguistics, pp. 174-181, Madrid, Spain(1997)
20. Wang,Araki: Modifying SO-PMI for JapaneseWeblog Opinion Mining by Using a Balancing Factor and Detecting Neutral Expressions. In: Proceedings of NAACL HLT 2007, pp. 189-192, Rochester, New York, US (2007)
21. Youngho, Kim, Sung-Hyon, Myaeng: Opinion Analysis based on Lexical Clues and their Expansion In: Proceedings of NTCIR-6 Workshop Meeting, pp 308-315, Tokyo, Japan (2007)

Sentiment Analysis Opinion Mining Affective Computing