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http://dx.doi.org/10.5916/jkosme.2015.39.3.274

Sentiment Analysis System Using Stanford Sentiment Treebank  

Lee, Songwook (Department of Computer Science and Information Engineering, Korea National University of Transportation)
Publication Information
Journal of Advanced Marine Engineering and Technology / v.39, no.3, 2015 , pp. 274-279 More about this Journal
Abstract
The main goal of this research is to build a sentiment analysis system which automatically determines user opinions of the Stanford Sentiment Treebank in terms of three sentiments such as positive, negative, and neutral. Firstly, sentiment sentences are POS tagged and parsed to dependency structures. All nodes of the Treebank and their polarities are automatically extracted from the Treebank. We train two Support Vector Machines models. One is for a node level classification and the other is for a sentence level. We have tried various type of features such as word lexicons, POS tags, Sentiment lexicons, head-modifier relations, and sibling relations. Though we acquired 74.2% in accuracy on the test set for 3 class node level classification and 67.0% for 3 class sentence level classification, our experimental results for 2 class classification are comparable to those of the state of art system using the same corpus.
Keywords
Sentiment analysis; Support vector machines; Stanford sentiment treebank;
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1 K. J. Lee, "Compositional rules of Korean auxiliary predicates for sentiment analysis," Journal of the Korean Society of Marine Engineering, vol. 37, no. 3, pp. 291-299, 2013.   DOI   ScienceOn
2 B. Liu, M. Hu, and J. Cheng, "Opinion observer : Analyzing and comparing opinions on the web," Proceedings of the 14th international World Wide Web conference, pp. 342-451, 2005.
3 A. M. Popescu and O. Etzioni, "Extracting product features and opinions from reviews," Proceedings of Conference on Empirical Methods on Natural Language Processing, pp. 339-346, 2005.
4 P. D. Turney, "Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews," Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics (ACL'02), pp. 417-424, 2002.
5 R. Socher, A. Perelygin, J. Y. Wu, J. Chuang, C. D. Manning, A. Y. Ng, and C. Potts Potts, "Recursive deep models for semantic compositionality over a sentiment treebank," Proceedings of Conference on Empirical Methods on Natural Language Processing, 2013.
6 J. Bollen, H. Mao, and X. J. Zeng, "Twitter mood predicts the stock market," Journal of Computational Science, vol. 2, no. 1, pp. 1-8, 2011.   DOI   ScienceOn
7 S. Asur and B. A. Huberman, "Predicting the future with social media," Proceedings of the 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, vol. 1, pp. 492-499, 2010.
8 A. Pak and P. Paroubek, "Twitter as a corpus for sentiment analysis and opinion mining," Proceedings of the Seventh Conference on International Language Resources and Evaluation (LREC'10), pp. 1320-1326, 2010.
9 A. Go, R. Bhayani, and L. Huang, "Twitter sentiment classification using distant supervision," Technical report CS224N, Stanford University, 2009.
10 B. O'Connor, R. Balasubramanyan, B. R. Routledge, and N. A. Smith, "From tweets to polls : Linking text sentiment to public opinion time series," Proceedings of the Fourth International AAAI Conference on Weblogs and Social Media, pp. 122-129, 2010.
11 C. D. Manning, M. Surdeanu, J. Bauer, J. Finkel, S. J. Bethard, and D. McClosky, "The stanford coreNLP natural language processing toolkit," Proceedings of 52nd Annual Meeting of the Association for Computational Linguistics : System Demonstrations, pp. 55-60, 2014.
12 C. C. Chang and C. J. Lin, "LIBSVM : a library for support vector machines," ACM Transactions on Intelligent Systems and Technology, vol. 2, no. 3, pp. 27:1-27:27, 2011.
13 M. Ganapathibhotla and B. Liu, "Mining opinions in comparative sentences," Proceedings of the 22nd International Conference on Computational Linguistics, pp. 18-22, 2008.