acl acl2013 acl2013-117 acl2013-117-reference knowledge-graph by maker-knowledge-mining

117 acl-2013-Detecting Turnarounds in Sentiment Analysis: Thwarting


Source: pdf

Author: Ankit Ramteke ; Akshat Malu ; Pushpak Bhattacharyya ; J. Saketha Nath

Abstract: Thwarting and sarcasm are two uncharted territories in sentiment analysis, the former because of the lack of training corpora and the latter because of the enormous amount of world knowledge it demands. In this paper, we propose a working definition of thwarting amenable to machine learning and create a system that detects if the document is thwarted or not. We focus on identifying thwarting in product reviews, especially in the camera domain. An ontology of the camera domain is created. Thwarting is looked upon as the phenomenon of polarity reversal at a higher level of ontology compared to the polarity expressed at the lower level. This notion of thwarting defined with respect to an ontology is novel, to the best of our knowledge. A rule based implementation building upon this idea forms our baseline. We show that machine learning with annotated corpora (thwarted/nonthwarted) is more effective than the rule based system. Because of the skewed distribution of thwarting, we adopt the Areaunder-the-Curve measure of performance. To the best of our knowledge, this is the first attempt at the difficult problem of thwarting detection, which we hope will at Akshat Malu Dept. of Computer Science & Engg., Indian Institute of Technology Bombay, Mumbai, India. akshatmalu@ cse .i itb .ac .in J. Saketha Nath Dept. of Computer Science & Engg., Indian Institute of Technology Bombay, Mumbai, India. s aketh@ cse .i itb .ac .in least provide a baseline system to compare against. 1 Credits The authors thank the lexicographers at Center for Indian Language Technology (CFILT) at IIT Bombay for their support for this work. 2


reference text

Blei, D. M., Ng, A. Y., and Jordan, M. I. 2003. Latent Dirichlet allocation. In the Journal of machine Learning research, 3, pages 993-1022. Brooke, J. 2009. A Semantic Approach to Automated Text Sentiment Analysis. Ph.D. thesis, Simon Fraser University. Chang, C. C., and Lin, C. J. 2011. LIBSVM: a library for support vector machines. ACM Transactions on Intelligent Systems and Technology (TIST),2(3), 27. Cohen, J. 1960. A coefficient of agreement for nominal scales. Educational and psychological measurement 20, no. 1, pages 37-46. Esuli, A. and Sebastiani, F. 2006. Sentiwordnet: A publicly available lexical resource for opinion mining. In Proceedings of LREC, Volume 6, pages 417-422. Hu, M. and Liu, B. 2004. Mining and summarizing customer reviews. In Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining, pages 168177. ACM. Klein, D. and Manning, C. D. 2003. Accurate Unlexicalized Parsing. In Proceedings of the 41st Meeting of the Association for Computational Linguistics, pages 423-430. Ling, C. X., Huang, J. and Zhang, H.2003. AUC: A better measure than accuracy in comparing learning algorithms. In Advances in Artificial Intelligence, pages 329-341, Springer Berlin Heidelberg. 864 Liu, B., and Zhang, L. 2012. A survey of opinion mining and sentiment analysis. In Mining Text Data (pp. 415-463).Springer US. Liu B., 2012. Sentiment analysis and opinion min- ing. Synthesis Lectures on Human Language nologies, 5(1), 1-167. Tech- Ohana, B. and Tierney, B. 2009.Sentiment classification of reviews using SentiWordNet. In 9th. IT & T Conference, page 13. Pang, B., and Lee, L. 2008. Opinion mining and sentiment analysis. Foundations and trends in information retrieval, 2(1-2), 1-135. Pang, B., Lee, L. and Vaithyanathan S. 2002. Thumbs up? Sentiment Classification using Machine Learning Techniques. In Proceedings of EMNLP pages 79-86). Polpinij, J. and Ghose, A. K. 2008.An ontology-based sentiment classification methodology for online consumer reviews. In Web Intelligence and Intelligent Agent Technology. Taboada, M. and Grieve, J. 2004. Analyzing appraisal automatically. In Proceedings of AAAI Spring Symposium on Exploring Attitude and Affect in Text (AAAI Technical Report SS# 04# 07), Stanford University, CA, pages. 158-161 . AAAI Press. Toutanova, K., Klein, D., Manning, C. D. and Singer Y. 2003. Feature-Rich Part-of-Speech Tagging with a Cyclic Dependency Network. In Proceedings of HLT-NAACL, pages 252-259. Tsur, O., Davidov, D., & Rappoport, A. 2010. ICWSM–A great catchy name: Semi-supervised recognition of sarcastic sentences in online product reviews. In Proceedings of the fourth international AAAI conference on weblogs and social media, pages. 162-169. Saggion, H., Funk, A., Maynard, D. and Bontcheva, K. 2007. Ontology-based information extraction for business intelligence. In The Semantic Web pages 843-856, Springer Berlin Heidelberg. Stone, P. J., Dunphy, D. C., Smith, M. S., Ogilvie, D. M. and Associates. 1966. The General Inquirer: A Computer Approach to Content Analysis. The MIT Press. 865