emnlp emnlp2011 emnlp2011-63 emnlp2011-63-reference knowledge-graph by maker-knowledge-mining

63 emnlp-2011-Harnessing WordNet Senses for Supervised Sentiment Classification


Source: pdf

Author: Balamurali AR ; Aditya Joshi ; Pushpak Bhattacharyya

Abstract: Traditional approaches to sentiment classification rely on lexical features, syntax-based features or a combination of the two. We propose semantic features using word senses for a supervised document-level sentiment classifier. To highlight the benefit of sense-based features, we compare word-based representation of documents with a sense-based representation where WordNet senses of the words are used as features. In addition, we highlight the benefit of senses by presenting a part-ofspeech-wise effect on sentiment classification. Finally, we show that even if a WSD engine disambiguates between a limited set of words in a document, a sentiment classifier still performs better than what it does in absence of sense annotation. Since word senses used as features show promise, we also examine the possibility of using similarity metrics defined on WordNet to address the problem of not finding a sense in the training corpus. We per- form experiments using three popular similarity metrics to mitigate the effect of unknown synsets in a test corpus by replacing them with similar synsets from the training corpus. The results show promising improvement with respect to the baseline.


reference text

Cem Akkaya, Janyce Wiebe, and Rada Mihalcea. 2009. Subjectivity word sense disambiguation. In Proc. of EMNLP ’09, pages 190–199, Singapore. Satanjeev Banerjee and Ted Pedersen. 2002. An adapted lesk algorithm for word sense disambiguation using wordnet. In Proc. of CICLing ’02, pages 136–145, London, UK. Jorge Carrillo de Albornoz, Laura Plaza, and Pablo Gervs. 2010. Improving emotional intensity classification using word sense disambiguation. Special issue: Natural Language Processing and its Applications. Journal on Research in Computing Science, 46: 131–142. Pdraig Cunningham. 2008. Dimension reduction. In Machine Learning Techniques for Multimedia, Cognitive Technologies, pages 91–1 12. Christiane Fellbaum. 1998. WordNet: An Electronic Lexical Database. Bradford Books. Stefan Gindl and Johannes Liegl, 2008. Evaluation of different sentiment detection methods for polarity classification on web-based reviews, pages 35–43. Alistair Kennedy and Diana Inkpen. 2006. Sentiment classification of movie reviews using contextual valence shifters. Computational Intelligence, 22(2): 110– 125. Mitesh Khapra, Sapan Shah, Piyush Kedia, and Pushpak Bhattacharyya. 2010. Domain-specific word sense disambiguation combining corpus basedand wordnet based parameters. In Proc. of GWC’10, Mumbai, India. Claudia Leacock and Martin Chodorow. 1998. Combining local context with wordnet similarity for word sense identification. In WordNet: A Lexical Reference System and its Application. Edda Leopold and J o¨rg Kindermann. 2002. Text categorization with support vector machines. how to represent texts in input space? Machine Learning, 46:423– 444. Dekang Lin. 1998. An information-theoretic definition of similarity. In In Proc. ofthe 15th International Con- ference on Machine Learning, pages 296–304. Tamara Martn-Wanton, Alexandra Balahur-Dobrescu, Andres Montoyo-Guijarro, and Aurora Pons-Porrata. 2010. Word sense disambiguation in opinion mining: Pros and cons. In Proc. of CICLing ’10, Madrid,Spain. Shotaro Matsumoto, Hiroya Takamura, and Manabu Okumura. 2005. Sentiment classification using word sub-sequences and dependency sub-trees. In Proc. of PAKDD’05,, Lecture Notes in Computer Science, pages 301–3 11. Bo Pang and Lillian Lee. 2004. A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts. In Proc. of ACL’04, pages 271–278, Barcelona, Spain. Bo Pang and Lillian Lee. 2008. Opinion mining and sentiment analysis. Found. Trends Inf. Retr., 2: 1–135. Bo Pang, Lillian Lee, and Shivakumar Vaithyanathan. 2002. Thumbs up? sentiment classification using machine learning techniques. volume 10, pages 79–86. Ted Pedersen, Siddharth Patwardhan, and Jason Michelizzi. 2004. Wordnet::similarity: measuring the relatedness of concepts. In Demonstration Papers at HLTNAACL’04, pages 38–41 . Vassiliki Rentoumi, George Giannakopoulos, Vangelis Karkaletsis, and George A. Vouros. 2009. Sentiment analysis of figurative language using a word sense disambiguation approach. 1091 In Proc. of the In- ternational Conference RANLP’09, pages 370–375, Borovets, Bulgaria. Peter Turney. 2002. Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews. In Proc. of ACL’02, pages 417–424, Philadelphia, US. Casey Whitelaw, Navendu Garg, and Shlomo Argamon. 2005. Using appraisal groups for sentiment analysis. In Proc. of CIKM ’05, pages 625–631, New York, NY, USA. Janyce Wiebe and Rada Mihalcea. 2006. Word sense and subjectivity. In Proc. of COLING-ACL’06, pages 1065–1072. Qiang Ye, Ziqiong Zhang, and Rob Law. 2009. Sentiment classification of online reviews to travel destinations by supervised machine learning approaches. Expert Systems with Applications, 36(3):6527 – 6535.