Sentiment Analysis of Twitter Data using Statistical Methods

Jahiruddin

Abstract

Social media today has becomes a very popular tool in society. Millions of users share their opinions on different topics like politics, technologies, product and many more. Therefore social media is a rich source of data for opinion mining and sentiment analysis. In this paper we use the twitter data for sentiment analysis. First we use the Latent Dirichlet Allocation (LDA) to identify the key terms. These key terms are used to represent each tweet in n dimensional vector. Using this tweet vectors, we build a sentiment classifier, which is able to determine positive, negative, and neutral sentiment of each tweet. Experimental result show that our proposed method is efficient and out performs.

Keywords

Twitter, Latent Dirichlet allocation, Opinion mining, Sentiment analysis, Key term identification.

References

[1] B. Pang and L. Lee, “Opinion mining and sentiment analysis,” Foundations and Trends in Information Retrieval, vol. 2, no. 1-2, pp. 1–135, 2008.

[2] C. Yang, K. H.-Y. Lin, and H.-H. Chen, “Emotion classification using web blog corpora,” in Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence, 2007, pp. 275–278.

[3] J. Read, “Using emoticons to reduce dependency in machine learning techniques for sentiment classification,” in Proceedings of the ACL Student Research Workshop, Ann Arbor, Michigan, 2005, pp. 43–48.

[4] A. Go, L. Huang, and R. Bhayani, “Twitter sentiment analysis,” Stanford University, Stanford, California, USA, CS224N - Final Project Report, 2009.

[5] H. Becker, M. Naaman, and L. Gravano, “Learning similarity metrics for event identification in social media,” in Proceedings of the third ACM international conference on Web search and data mining, 2010, pp. 291–300.

[6] T. Sakaki, M. Okazaki, and Y. Matsuo, “Earthquake shakes twitter users: Real-time event detection by social sensors,” in Proceedings of the 19th international conference on World Wide Web, 2010, pp. 851–860.

[7] J. Sankaranarayanan, H. Samet, B. E. Teitler, M. D. Lieberman, and J. Sperling, “witterstand: news in tweets,” in Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, 2009, pp. 42–51.

[8] H. Becker, M. Naaman, and L. Gravano, “Beyond trending topics: Real-world event identification on twitter,” in Proceedings of the Fifth International AAAI Conference on Weblogs and Social Media, 2011, pp. 438–441.

[9] G.E. Forsythe, M.A. Malcolm, and C.B. Moler, Computer Methods for Mathematical Computations, Prentice Hall Professional Technical Reference, ISBN:0131653326, 1977.

[10] D. M. Blei, A. Y. Ng, and M. I. Jordan, “Latent dirichlet allocation,” Journal of Machine Learning Research, vol. 3, no. 4-5, pp. 993–1022, 2003.

[11] M. Abulaish, Jahiruddin, and L. Dey, “Deep text mining for automatic keyphrase extraction from text documents,” Journal of Intelligent Systems, vol. 20, no. 4, pp. 327–351, 2011.

Cites this article as

J. "Sentiment Analysis of Twitter Data using Statistical Methods", International Journal of Innovative Research in Engineering & Management (IJIREM), Vol-2, Issue-4, Page No-30-34, 2015. Available from: