Srinvas A, Hanumanthappa M


Sentiment analysis is a process of extracting, identifying and categorizing a writer’s emotion, expressed in the form of text, by implying a computational method. This paper presents a study of various modern approaches for sentiment analysis along with the hurdles and possible solutions present in these approaches. Further, the study is concentrated on two main categories, machine learning and lexicon analysis, for sentiment analysis. Even though, the various methods falling under these two main approaches are elaborated and illustrated, the supervised learning method of machine learning is more concentrated in the article. This paper also describes a generalized sentiment analysis method that can be incorporated with any of the existing analytical algorithms for sentiment analysis as well as any mundane text analysis.


Sentiment analysis; machine learning; lexicon analysis; supervised learning; text analysis.

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Douglas Rice, Christopher Zorn, “Corpus-based dictionaries for sentiment analysis of specialized vocabularies”, New Directions in Analyzing Text as Data Workshop, London, ver 0.1, September 2013.

Bo Pang, Lillian Lee, Shivakumar Vaithyanathan, “Thumbs up? sentiment classification using machine learning techniques”, Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), pp 79–86, 2002.

Vryniotis, Vasilis, “The importance of Neutral Class in Sentiment Analysis”, Machine Learning and Statistics, September, 2013.

Koppel, Moshe, Schler, Jonathan, “The Importance of Neutral Examples for Learning Sentiment”, Computational Intelligence 22. pp. 100–109, 2006.

J. R. Quilan, “Induction of decision trees”, Machine learning, pp. 81-106, 1986.

M. K. S. Varma, N. K. K. Rao, K. K. Raju,G. P. S. Varma, “Pixel-Based Classification Using Support Vector Machine Classifier”, Preceedings of the IEEE 6th International Conference on Advanced Computing (IACC), August, 2016.

S. Samarasinghe, “Neural networks for applied sciences and engineering: from fundamentals to complex pattern recognition”, Aurebach Publications, April, 2016.

Aggarwal Charu C, Zhai Cheng Xiang, "Mining Text Data", Springer New York Dordrecht Heidelberg London, Springer Science, LLC’12, 2012.

Fu Xianghua, Liu Guo, Guo Yanyan, Wang Zhiqiang, "Multi-aspect sentiment analysis for Chinese online social reviews based on topic modeling and HowNet lexicon", Knowledge Based Systems,pp. 186-195, 2013.

Hu Minging, Liu Bing, "Mining and summarizing customer reviews", Proceedings of ACM SIGKDD international conference on Knowledge Discovery and Data Mining, 2004.

Walaa Medhat, Ahmed Hassan, Hoda Korashy, "Sentiment analysis algorithms and applications: A survey", Ain Shams Engineering Journal, pp. 1093-1113, Volume 5, Issue 4, December 2014.

Isa Maks, Piek Vossen, "A lexicon model for deep sentiment analysis and opinion mining applications",Decision Support Systems, pp. 680-688, 2012.



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