Random Articles
Reseach Article
Design of Sentiment Analysis System using Polarity Classification Technique
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 125 - Number 15 |
| Year of Publication: 2015 |
| Authors: Rajeshwar Rao Kodipaka, Sanjeeva Polepaka, Md. Rafeeq |
10.5120/ijca2015906159
|
Rajeshwar Rao Kodipaka, Sanjeeva Polepaka, Md. Rafeeq . Design of Sentiment Analysis System using Polarity Classification Technique. International Journal of Computer Applications. 125, 15 ( September 2015), 22-24. DOI=10.5120/ijca2015906159
Abstract
Twitter is a medium that we can use for communication. All posted tweets we can store in one location and create archive. Archive contains new and old tweets. Now we can start the analyzation on archive tweets that’s we can design effective sentiment analysis system. This paper main aim is to determine parts of speech opinion words using polarity classification technique and support vector machine learning algorithm. Surveys of methods are used in various levels of sentiment analysis. It does analyze the tweets information in limited levels of content only. Now in this paper we design new sentiment analysis tool using polarity classification technique. Polarity classification techniques discover top 20-emoticons, learning different classes of words and other features information. These techniques perform in depth tweets analysis. It does provide better analysis results compare to previous methods.
References
- B. J. Jansen, M. Zhang, K. Sobel, and A. Chowdury, “Twitter power: Tweets as electronic word of mouth,” J. Am. Soc. Inform.Sci. Technol., vol. 60, no. 11, pp. 2169–2188, 2009.
- B. J. Jansen, M. Zhang, K. Sobel, and A. Chowdury, “Micro-blogging as online word of mouth branding,” in Proc. Extended Abstr. Human Factors Comput. Syst., 2009, pp. 3859–3864.
- J. Bollen, H. Mao, and X. Zeng, “Twitter mood predicts the stock market,” J. Comput. Sci., vol. 2, no. 1, pp. 1–8, 2011.
- A. Tumasjan, T. O. Sprenger, P. G. Sandner, and I. M. Welpe, “Predicting elections with twitter: What 140 characters reveal about political sentiment,” in Proc. 4th Int. AAAI Conf. Weblogs Soc. Media, 2010, vol. 10, pp. 178–185.
- L. T. Nguyen, P. Wu, W. Chan, W. Peng, and Y. Zhang, “Predicting collective sentiment dynamics from time-series social media,” in Proc. 1st Int. Workshop Issues Sentiment Discovery Opinion Mining, 2012, p. 6.
- M. Thelwall, K. Buckley, and G. Paltoglou, “Sentiment in twitter events,” J. Am. Soc. Inform. Sci. Technol., vol. 62, no. 2, pp. 406–418, 2011.
- A. Agarwal, B. Xie, I. Vovsha, O. Rambow, and R. Passonneau, “Sentiment analysis of twitter data,” in Proc. Workshop Lang. Soc. Media, 2011, pp. 30–38.
- B. Liu, “Sentiment analysis and opinion mining,” Synthesis Lect. Human Lang. Technol., vol. 5, no. 1, pp. 1–167, 2012.
- C. Tan, L. Lee, J. Tang, L. Jiang, M. Zhou, and P. Li, “User-level sentiment analysis incorporating social networks,” in Proc. 17th ACM SIGKDD Int. Conf. Knowl. Discovery Data Mining, 2011, pp. 1397–1405.
- J. Blitzer, M. Dredze, and F. Pereira, “Biographies, bollywood, boom-boxes and blenders: Domain adaptation for sentiment classification,” in Proc. 45th Annu. Meeting Assoc. Comput. Linguistics, 2007, vol. 7, pp. 440–447.
- F. Li, S. J. Pan, O. Jin, Q. Yang, and X. Zhu, “Cross-domain coextraction of sentiment and topic lexicons,” in Proc. 50th Annu. Meeting Assoc. Comput. Linguistics: Long Papers, 2012, pp. 410–419.
- S. J. Pan, X. Ni, J.-T. Sun, Q. Yang, and Z. Chen, “Cross-domain sentiment classification via spectral feature alignment,” in Proc. 19th Int. Conf. World Wide Web, 2010, pp. 751–760.
- I. Ounis, C. Macdonald, J. Lin, and I. Soboroff, “Overview of the trec-2011 microblog track,” in Proc. 20th Text Retrieval Conf., 2011, http://trec.nist.gov/pubs/trec20/t20.proceedings.html
- I. Soboroff, I. Ounis, J. Lin, and I. Soboroff, “Overview of the trec- 2012 microblog track,” in Proc. 21st Text REtrieval Conf., 2012.
- A. Go, R. Bhayani, and L. Huang, “Twitter sentiment classification using distant supervision,” CS224N Project Report, Computer Science Department, Stanford, USA, pp. 1–12, 2009.
- S. Li, C.-R. Huang, G. Zhou, and S. Y. M. Lee, “Employing personal/ impersonal views in supervised and semi-supervised sentiment classification,” in Proc. 48th Annu. Meeting Assoc. Comput. Linguistics, 2010, pp. 414–423.
Index Terms
Keywords