Anuj Sharma
Shubhamoy Dey
ABSTRACT
The Internet and Web 2.0 social media have emerged as an important medium for expressing sentiments, opinions, evaluations, and reviews. Sentiment analysis or opinion mining is becoming an open research domain due to the abundance of discussion forums, Weblogs, e-commerce portals, social networking and content sharing sites where people tend to express their opinions. Sentiment Analysis involves classifying text documents based on the opinion expressed being positive or negative about a given topic. This paper proposes a sentiment classification model using back-propagation artificial neural network (BPANN). Information Gain and three popular sentiment lexicons are used to extract sentiment representing features that are then used to train and test the BPANN. This novel approach combines the strength of BPANN in classification accuracy with utilizing intrinsic domain knowledge available in the sentiment lexicons. The results obtained on the movie-review corpora have shown that the proposed approach has been able to reduce dimensionality, while producing accurate sentiment based classification of text.
- Annett, M., and Kondrak, G. A comparison of sentiment analysis techniques: Polarizing movie blogs. Advances in Artificial Intelligence, (2008), 5032, 25--35. Google Scholar
Digital Library
- Bai, X. Predicting consumer sentiments from online text. Decision Support Systems, 50, 4 (2011), 732--742. Google ScholarDigital Library
- Balog, K., Mishne, G., and de Rijke, M. Why are they excited?: Identifying and explaining spikes in blog mood levels. In Proceedings of EACL (Morristown, NJ, USA) ACL, 2006, 207--210. Google ScholarDigital Library
- Boiy, E., Hens, P., Deschacht, K., and Moens, M. F. Automatic sentiment analysis of on-line text. In Proceedings of the 11th International Conference on Electronic Publishing (Vienna, Austria) 2007.Google Scholar
- Chevalier, J. A., and Mayzlin, D. The effect of word of mouth on sales: Online book reviews. Journal of Marketing Research, 43, 3 (2006), 345--354.Google ScholarCross Ref
- Conrad, J. G., and Schilder, F. Opinion mining in legal blogs. In Proceedings of 11th International Conference on Artificial Intelligence and Law (ICAIL '07) (New York) ACM, 2007, 231--236. Google ScholarDigital Library
- Cui, H., Mittal, V., and Datar, M. Comparative experiments on sentiment classification for online product reviews. In Proceedings of AAAI (Boston, Massachusetts, July 16--20, 2006). 2006, 1265--1270. Google ScholarDigital Library
- Dave, K., Lawrence, S., and Pennock, D. M. Mining the peanut gallery: opinion extraction and semantic classification of product reviews. In Proceedings of the 12th international WWW conference (Budapest, Hungary, May 20--24, 2003). 2003, 519--528. Google ScholarDigital Library
- Decker, R., and Trusov, M. Estimating aggregate consumer preferences from online product reviews. International Journal of Research in Marketing, 27, 4 (2010), 293--307.Google ScholarCross Ref
- Ding, X., and Liu, B. The utility of linguistic rules in opinion mining. In Proceedings of the 30th annual international ACM SIGIR conference on research and development in information retrieval (SIGIR '07) 2007, 811--812. Google ScholarDigital Library
- Esuli, A., and Sebastiani, F. Determining the semantic orientation of terms through gloss classification. In Proceedings of the 14th ACM international conference on information and knowledge management. (CIKM '05) 2005, 617--624. Google ScholarDigital Library
- Godbole, N., Srinivasaiah, M., and Skiena, S. Large-scale sentiment analysis for news and blogs. In Proceedings of the International Conference on Weblogs and Social Media (ICWSM'07) 2007.Google Scholar
- Hatzivassiloglou, V., and McKeown, K. R. Predicting the semantic orientation of adjectives. In Proceedings of the 35th ACL/8th EACL. 1997, 174--181. Google ScholarDigital Library
- Hu, M., and Liu, B. Mining and summarizing customer reviews. In Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining (Seattle, WA, USA). ACM, 2004. Google ScholarDigital Library
- Kamps, J., Marx, M., Mokken, R. J., and De Rijke, M. Using wordnet to measure semantic orientations of adjectives. In Proceedings of 4th International Conference on Language Resources and Evaluation (Lisbon, PT). 2004, 1115--1118.Google Scholar
- Kang, H., Yoo, S. J., and Han, D. Senti-lexicon and improved Naïve Bayes algorithms for sentiment analysis of restaurant reviews. Expert Systems with Applications (2011), doi:10.1016/j.eswa.2011.11.107. Google ScholarDigital Library
- Osherenko, A., and André, E. Lexical affect sensing: Are affect dictionaries necessary to analyze affect? In Proceedings of the 2nd international conference on affective computing and intelligent interaction (ACII'07) Springer-Verlag, Berlin. 2007, 230--241. Google ScholarDigital Library
- Paltoglou, G., Gobron, S., Skowron, M., Thelwall, M. and Thalmann, D. Sentiment analysis of informal textual communication in cyberspace. In Proceedings of Engage '10. 2010, 13--25.Google Scholar
- Pang, B., and Lee, L. A sentimental education: sentiment analysis using subjectivity summarization based on minimum cuts. In Proceedings of the 42nd annual meeting of the Association for Computational Linguistics (ACL) (Barcelona, Spain, July 21--26, 2004). 2004, 271--278. Google ScholarDigital Library
- Pang, B., and Lee, L. Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval, 2(1--2), (2008), 1--135. Google ScholarDigital Library
- Pang, B., and Lee, L. Seeing stars: exploiting class relationships for sentiment categorization with respect to rating scales. In Proceedings of the 43rd annual meeting of the ACL (University of Michigan, USA, June 25--30, 2005). 2005, 115--124. Google ScholarDigital Library
- Pang, B., Lee, L., and Vaithyanathan, S. Thumbs up? sentiment classification using machine learning techniques. In Proceedings of the ACL-02 conference on Empirical methods in natural language processing. ACL, 2002. Volume 10. Google ScholarDigital Library
- Prabowo, R., and Thelwall, M. Sentiment analysis: A combined approach. Journal of Informetrics, 3(2), (2009), 143--157.Google Scholar
- Rumelhart, D. E., and McClelland, J. L. Parallel distributed processing. MIT Press Cambridge and the PDP Research Group, 1986.Google ScholarDigital Library
- Stone, P. J., Dunphy, D. C., Smith, M. S., and Ogilvie, D. M. The general inquirer: A computer approach to content analysis. MIT Press Cambridge, MA, 1966.Google Scholar
- Tan, S., and Zhang, J. An empirical study of sentiment analysis for Chinese documents. Expert Systems with Applications, 34, 4 (2008), 2622--2629. Google ScholarDigital Library
- Thomas, M., Pang, B., and Lee, L. Get out the vote: determining support or opposition from congressional floor-debate transcripts. In Proceedings of the 2006 conference on empirical methods in natural language processing (EMNLP 2006) (Sydney). 2006, 327--335. Google ScholarDigital Library
- Tsytsarau, M., and Palpanas, T. Survey on mining subjective data on the web. Data Mining and Knowledge Discovery (2011), 1--37. Google ScholarDigital Library
- Turney, P. D. Thumbs up or thumbs down?: semantic orientation applied to unsupervised classification of reviews. In Proceedings of the 40th annual meeting on ACL (ACL'02) (Morristown, NJ, USA) ACL, 2002, 417--424. Google ScholarDigital Library
- Turney, P. D., and Littman, M. L. Measuring praise and criticism: Inference of semantic orientation from association. ACM Trans. on Info. Systems, 21, 4 (2003), 315--346. Google ScholarDigital Library
- Wiebe, J. M. Learning subjective adjectives from corpora. In Proceedings of the 17th national conference on artificial intelligence and 12th conference on innovative applications of artificial intelligence. AAAI Press/The MIT Press, 2000, 735--740. Google ScholarDigital Library
- Zhu, F., and Zhang, X. Impact of online consumer reviews on sales: The moderating role of product and consumer characteristics. Journal of Marketing, 74, 2 (2010), 133--148.Google ScholarCross Ref
Index Terms
- An artificial neural network based approach for sentiment analysis of opinionated text
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