Abstract

Sentiment analysis refers to a broad range of fields of the natural language processing, computational linguistics, and text mining. Mining is used to extract previously unknown information from the different written resources. This extracted information is helping in decision making process. Sentiment analysis has gained much attention in recent years. It determines the opinion and attitude of the people towards a particular topic. This paper focuses to improve the accuracy by using the optimal feature and reduces the complexity by Naïve Bayes classifier. In proposed work, comparing the results with the existing model regarding the accuracy, precision, recall and f-measure which shows that performance are improved in each and every case.

References

• Valakunde, N.D and Patwardhan, M.S. 2013. Multi-Aspect and Multi-Class Based Document Sentiment Analysis of Educational Data Catering Accreditation Process. International Conference on Cloud & Ubiquitous Computing & Emerging Technologies, pp.188-192.
• Pavitra, R. and PCD, Kalaivaani. 2015. Weakly supervised sentiment analysis using joint sentiment topic detection with bigrams. IEEE sponsored 2nd International Conference on electronics and communication system (ICECS ‘2015).
• Mouthami, K. and Nirmala, K.D. 2013.Sentiment Analysis and Classification Based On Textual Reviews. International Conference on Information Communication and Embedded Systems (ICICES), pp.271-276.
• Ahmad, S.R., Bakar, A. and Yaakub, M.R. 2015. Metaheuristic Algorithms for Feature Selection in Sentiment Analysis. Science and Information Conference 2015 July 28-30 | London, UK..
• Dar, A. and Jain, A. 2014. Survey paper on Sentiment Analysis: In General Terms International Journal of Emerging Research in Management & Technology ISSN: 2278-9359, Vol-3, Issue-11.
• Virmani, D., Taneja, S. and Bhatia, P. 2015. Aspect Level Sentiment Analysis to Distil Scrupulous Opinionated Result. International Conference on Computing, Communication and Automation, pp. 59-65.
• Hajmohammadi, M.S., Ibrahim, R., Selamat, A. and Fujita, H. 2015. Combination of active learning and self-training for cross-lingual sentiment classification with density analysis of unlabelled samples. Information Sciences 317, pp. 67–77.
• Khan, A. and Baharudin, B. 2011. Sentiment classification using sentence-level semantic orientation of opinion terms from blogs. IEEE National Postgraduate Conference.
• Poria, S., Cambria, E., Howard, N. and Hussain, A. 2016. Fusing audio, visual and textual clues for sentiment analysis from multimodal content. Neurocomputing 174, pp. 50–59.
• Karamibekr, M. and Ghorbani, A.A. 2012.sentiment analysis of social issues. International Conference on social informatics, pp.215-221.
• Ren, F., Wu, Y. 2013. Predicting user-topic opinions in twitter with social and topical context, IEEE Trans. Affect. Comput. 4(4) 412–424.
• Han, J. and Kamber, M. 2001.Data Mining Concepts and Technique.
• Ye, J., Povinelli, R.J and Johnson, M.T. 2002 Phoneme classification using naive bayes classifier in reconstructed phase space. Proc. of IEEE Signal Processing Society 10th Digital Signal Processing Workshop, pp. 37- 40.
• Schouten, K. and Frasincar, F. (2015). Survey on Aspect-Level Sentiment Analysis. IEEE Transactions on Knowledge and Data engineering.
• Rocha, L., Mourao, F., Ferreira, R. (2015). SACI: Sentiment analysis by collective inspection on social media content. Web Semantics: Science, Services and Agents on the World Wide Web 34, 2015, pp. 27–39.