The growing popularity of social media, E-commerce, blogs and any social field created a new platform where anyone can discuss and exchange his/her views, ideas , suggestions and experiences about any product or services in market. This state of affairs open a new area of research called Opinion Mining and Sentiment Analysis. Opinion Mining and Sentiment Analysis is an extension of Data Mining that extracts and analyzes the unstructured data automatically. In this review paper our aim is to present the details study over Opinion Mining and Sentiment Analysis, its different techniques , methods etc.

Introduction, Sentiment analysis techniques , Literature review, Comparative analysis , Conclusion

[1] Mejova, Y. (2009). Sentiment Analysis: An Overview. Comprehensive exam paper, available http:// www. cs. uiowa. edu/~ ymejova/ publications/ Com ps Yelena Mejova. pdf [2010-02-03].
[2] X Fang, J Zhan” Sentiment analysis using product review data” , Journal of Big Data , Issue 1/2015,2015.
[3] M. Levene , A. Mudinas, D. Zhang, “Combining lexicon and learning based approaches for concept leve lsentiment analysis”, Proceedings of the First International Workshop on Issues of Sentiment Discovery and Opinion Mining, ACM, New York ,NY, USA, Article 5, pp. 1-8,2 012.
[4] P. D. Turney, “Thumbs up or thumbs down? Semantic orientation applied to unsupervised classificationofreviews”,40th annual meeting, association for computational linguistics “ , Association for Computational Linguistics, pp. 417– 424,2002.
[5] A Kamps, “sentiment analysis in twitter using machine learning techniques”, Fourth International Conference on Computing, Communications and , Tiruchengode, pp. 1-5, 2013.
[6] D. Pucci, M. Baroni, F. Cutugno, and A. Lenci, “Unsupervised lexical substitution with a word space model,” in presenting of EVALITA workshop, 11th Congress of Italian Association for Artificial Intelligence ,Citeseer , 2009.
[7] A. Balahur, J. M. Hermida, and A. Montoyo, “Building and exploiting emoti- net, a knowledge base for emotion detection based on the appraisal theory model,” Affective Computing, IEEE Transactions on, vol. 3, no. 1,pp. 88– 101, 2012.
[8] P. Domingos , M. Pazzani presnsented , “On the optimality of the simple bayesian classifier under zero-one loss,” Machine Learning, 29, 103–130 (1997), Kluwer Academic Publishers.,Netherlands.
[9] Z. Niu, Z. Yin, and X. Kong, “Sentiment classification for micro blog by machine learning,” in the proceeding of the Computational and Information Sciences (ICCIS), Fourth International Conference on, pp. 286–289, IEEE, 2012.
[10] J. Feng ,L. Barbosa discovered “Robust sentiment detection on twitter from biased and noisy data,” in 23rd International Conference on Computational Linguistics: Posters, pp. 36–44, Association for Computational Linguistics, 2010.
[11] J. Feng ,D. Hakkani-Tur, , A. Celikyilmaz, “Probabilistic model- based sentiment analysis of twitter messages,” in Spoken Language Technology Workshop (SLT), 2010 IEEE, pp. 79–84, IEEE, 2010. 17).
[12] F. Ren, and Y. “Learning sentimental influence in twitter,” in the proceedings of the 2016 International Conference on Future Computer Sciences and Application (ICFCSA), pp. 119– 122, IEEE, 2011.
[13] A. Pak and P. ParoubekK, “Twitter as a corpus for sentiment analysis and opinion mining,” in LREC, vol. 2010, 2010.
[14] Rui Xia, ChengqingZong, Shoushan Li, “Ensemble of feature sets and classification algorithms for sentiment classification”, Information Sciences 181 (2011)1138–1152.
[15] Melville, WojciechGryc, “Sentiment Analysis of Blogs by Combining Lexical Knowledge with Text Classification”, KDD‟09, June 28–July 1, 2009, Paris, France.
[16] Ziqiong Zhang, Qiang Ye, Zili Zhang, Yijun Li,“Sentiment classification of Internet restaurant reviews written in Cantonese”, Expert Systems with Applications xxx (2011) xxx–xxx.
[17] Qing Ye, Ziqiong Zhang, Rob Law, “Sentiment classification of online reviews to travel destinations by supervised machine learning approaches”, Expert Systems with Applications 36 (2009) 6527– 6535.
[18] Songbo Tan, Jin Zhang, “An empirical study of sentiment analysis for chinese documents”, Expert Systems with Applications 34 (2008) 2622–2629.
[19] Ziqiong Zhang, Rob Law ,Qiang Ye, “Sentiment classification of • online reviews to travel destinations by supervised machine learning approaches”, Expert Systems with Applications 36 (2009) 6527– 6535.
[20] Ion SMEUREANU, Cristian BUCUR, “Applying Supervised Opinion Mining Techniques on Online User Reviews”, Informatical Economic vol. 16, no. 2/2012.
[21] Rudy Prabowo, Mike Thelwall, “Sentiment analysis: A combined approach.” Journal of Informetrics 3 (2009)143–157.
[22] Hui-Ju Chiu ,Long-Sheng Chen, Cheng-Hsiang Liu, “A neural network based approach for sentiment classification in the blogosphere”, Journal of Info 5 (2011).
[23] S. Vaithyanathan, and B. Pang, L. Lee, and “Thumbs up?: sentiment classification using machine learning techniques,” Proceedings of the ACL-02 conference on Empirical methods in natural language processing, 2002.
[24] B.Liu, Zhang, R. Ghosh, M. Dekhil “Combining Lexicon-based and Learning-based Methods for Twitter Sentiment Analysis”, Technical report, HP Laboratories,2011
[25] A. Abbasi, H. Chen, and A. Salem, “Sentiment analysis in multiple languages: Feature selection for opinion classification in web forums,” In ACM Transactions on Information Systems, vol. 26 Issue3,pp.1 34,2008.
[26] S. Li , R. Xia, C. Zong, “Ensemble of feature sets and classification algorithms for sentiment classification,” Information Sciences: an International Journal, vol. 181, no. 6, pp. 1138–1152, 2011.
[27] A Palve, R. D.Sonawane, A.D. Potgantwar,” Sentiment Analysis of Twitter Streaming Data for Recommendation using, Apache Spark”, International journal of scientific research in network security and communication (IJSRNSC), Vol.5 , Issue.3 , pp.99-103, Jun-2017.
K .Sarvakar, U. K Kuchara “Sentiment Analysis of movie reviews: A new feature-based sentiment classification”, Isroset-Journal (IJSRCSE) Vol.6 , Issue.3 , pp.8-12, Jun-2018.