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http://dx.doi.org/10.3745/KTSDE.2013.2.10.731

Twitter Sentiment Analysis for the Recent Trend Extracted from the Newspaper Article  

Lee, Gyoung Ho (충남대학교 정보통신공학과)
Lee, Kong Joo (충남대학교 정보통신공학과)
Publication Information
KIPS Transactions on Software and Data Engineering / v.2, no.10, 2013 , pp. 731-738 More about this Journal
Abstract
We analyze public opinion via a sentiment analysis of tweets collected by using recent topic keywords extracted from newspaper articles. Newspaper articles collected within a certain period of time are clustered by using K-means algorithm and topic keywords for each cluster are extracted by using term frequency. A sentiment analyzer learned by a machine learning method can classify tweets according to their polarity values. We have an assumption that tweets collected by using these topic keywords deal with the same topics as the newspaper articles mentioned if the tweets and the newspapers are generated around the same time. and we tried to verify the validity of this assumption.
Keywords
Twitter; Sentiment Analysis; Tweet Topic; Clustering;
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1 J. Dimmick., Y. Chen, and Z. Li, "Competition between the Internet and traditional news media: The gratification opportunities niche dimension," in The Journal of Media Economic, 17.1, pp.19-33, 2004.   DOI   ScienceOn
2 D. A. Shamma, L. Kennedy, and E. F. Churchill, "Tweet the debates: understanding community annotation of uncollected sources," in Proceedings of the first SIGMM workshop on Social media. ACM, pp.3-10, 2009.
3 Turney and D. Peter, "Thumbs up or thumbs down?: semantic orientation applied to unsupervised classification of reviews," in Proceedings of the 40th annual meeting on association for computational linguistics. Association for Computational Linguistics, pp.417-424, 2002.
4 G. Dray, M. Plantie, A. Harb, P. Poncelet, M. Roche, and F. Trousset, "Opinion mining from blogs," in IJCISIM'09: International Journal of Computer Information Systems and Industrial Management Applications, 1, pp.205-213, 2009.
5 N. Godbole, M. Srinivasaiah, and S. Skiena, "Large-scale sentiment analysis for news and blogs," in Proceedings of the International Conference on Weblogs and Social Media (ICWSM), Vol.2, 2007.
6 A. Bakliwal, P. Arora, S. Madhappan, N. Kapre, M. Singh, and V. Varma, "Mining sentiments from Tweets," in WASSA 2012, pp.11-18, 2012.
7 A. Go, B. Richa, and H. Lei. "Twitter sentiment classification using distant supervision," in CS224N Project Report, Stanford, pp.1-12, 2009.
8 N. N. Bora, "Summarizing Public Opinions in Tweets," in Journal Proceedings of CICLing, 2012.
9 A. Agarwal, B. Xie, I. Vovsha, O. Rambow, and R. Passonneau, "Sentiment analysis of twitter data," in Proceedings of the Workshop on Languages in Social Media. Association for Computational Linguistics, pp.30-38, 2011.
10 L. Jiang, M. Yu, M. Zhou, X. Liu, and T. Zhao, "Target-dependent twitter sentiment classification," in Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, Vol.1, pp.151-160, 2011.
11 L. B. Batista, and S. Ratte, "A Multi-Classifier System for Sentiment Analysis and Opinion Mining," in Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining. pp.96-100, 2012.
12 R. C. Prati, G. E. A. P. A. Batista, and M. C. Monard, "A survey on graphical methods for classification predictive performance evaluation," in Knowledge and Data Engineering, IEEE Transactions, pp.1601-1618, 2011.
13 W. Khreich, E. Granger, A. Miri, and R. Sabourin, "Iterative Boolean combination of classifiers in the ROC space: An application to anomaly detection with HMMs," in Pattern Recognition, 43(8), pp.2732-2752, 2010.   DOI   ScienceOn
14 J. Li, and K. Zhang, "Keyword extraction based on tf/idf for Chinese news document," in Wuhan University Journal of Natural Sciences, pp.917-921, 2007.
15 M. J. Pazzani, J. Muramatsu, and D. Billsus, "Syskill & Webert: Identifying interesting web sites," in Proceedings of the national conference on artificial intelligence. pp.54-61, 1996.
16 P. S. Bradley, and U. M. Fayyad, "Refining initial points for k-means clustering," in Proceedings of the fifteenth international conference on machine learning. Vol.66, pp.91-99, 1998.
17 C. D. Manning, P. Raghavan, and H. Schutze, "Introduction to information retrieval," Vol.1. Cambridge: Cambridge University Press, ch8, pp.148, 2008.