Abstract
Sentiment analysis has gained a lot of attention in recent years, mainly due to the many practical applications it supports and a growing demand for such applications. This growing demand is supported by an increasing amount and availability of opinionated online information, mainly due to the proliferation and popularity of social media. The majority of work in sentiment analysis considers the polarity of word terms rather than the polarity of specific senses of the word in context. However there has been an increased effort in distinguishing between different senses of a word as well as their different opinion-related properties. Syntactic parse trees are a widely used natural language processing construct that has been effectively employed for text classification tasks. This paper proposes a novel methodology for extending syntactic parse trees, based on word sense disambiguation and context specific opinion-related features. We evaluate the methodology on three publicly available corpuses, by employing the sub-set tree kernel as a similarity function in a support vector machine. We also evaluate the effectiveness of several publicly available sense specific sentiment lexicons. Experimental results show that all our extended parse tree representations surpass the baseline performance for every measure and across all corpuses, and compared well to other state-of-the-art techniques.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
Keywords
References
Agerri, R., Garc, A.: Q-WordNet: Extracting polarity from WordNet senses. In: Seventh Conference on International Language Resources and Evaluation Malta (2009)
Akkaya, C., Wiebe, J., Conrad, A., Mihalcea, R.: Improving the impact of subjectivity word sense disambiguation on contextual opinion analysis. In: Proceedings of the Fifteenth Conference on Computational Natural Language Learning, pp. 87–96. Association for Computational Linguistics (2011)
Baccianella, S., Esuli, A., Sebastiani, F.: SentiWordNet 3.0: An Enhanced Lexical Resource for Sentiment Analysis and Opinion Mining. In: Proceedings of the Seventh conference on International Language Resources and Evaluation LREC 2010 European Language Resources Association (ELRA), pp. 2200–2204 (2008)
Balamurali, A., Joshi, A., Bhattacharyya, P.: Robust Sense-Based Sentiment Classification. In: ACL HLT 2011, p. 132 (2011)
Balamurali, A.R., Joshi, A., Bhattacharyya, P.: Harnessing WordNet senses for supervised sentiment classification. In: Conference on Empirical Methods in Natural Language Processing, EMNLP 2011, July 27-31, pp. 1081–1091. Association for Computational Linguistics (ACL), Edinburgh (2011)
Carrillo de Albornoz, J., Plaza, L., Gervás, P.: A hybrid approach to emotional sentence polarity and intensity classification. In: Proceedings of the Fourteenth Conference on Computational Natural Language Learning, pp. 153–161. Association for Computational Linguistics (2010)
Cerini, S., Compagnoni, V., Demontis, A., Formentelli, M., Gandini, G.: Micro-WNOp: A gold standard for the evaluation of automatically compiled lexical resources for opinion mining. In: Language Resources and Linguistic Theory: Typology, Second Language Acquisition, English linguistics, Franco Angeli Editore, Milano, IT (2007)
Collins, M., Duffy, N.: New ranking algorithms for parsing and tagging: Kernels over discrete structures, and the voted perceptron. In: Proceedings of the 40th Annual Meeting on Association for Computational Linguistics, pp. 263–270. Association for Computational Linguistics (2002)
Esuli, A., Sebastiani, F.: Sentiwordnet: A publicly available lexical resource for opinion mining. In: Proceedings of LREC, pp. 417–422. Citeseer (2006)
Hu, M., Liu, B.: Mining and summarizing customer reviews. In: Proceedings of the tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 168–177. ACM (2004)
Liu, B.: Sentiment analysis and subjectivity. In: Handbook of Natural Language Processing (2010)
Martın-Wanton, T., Balahur-Dobrescu, A., Montoyo-Guijarro, A., Pons-Porrata, A.: Word sense disambiguation in opinion mining: Pros and cons. In: Special Issue: Natural Language Processing and its Applications, pp. 119–130 (2010)
Miller, G.A.: WordNet: a lexical database for English. Communications of the ACM 38(11), 39–41 (1995)
Moschitti, A.: Efficient convolution kernels for dependency and constituent syntactic trees. In: Fürnkranz, J., Scheffer, T., Spiliopoulou, M. (eds.) ECML 2006. LNCS (LNAI), vol. 4212, pp. 318–329. Springer, Heidelberg (2006)
Moschitti, A.: Making tree kernels practical for natural language learning. In: Proceedings of EACL, pp. 113–120 (2006)
Pang, B., Lee, L.: A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts. In: Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics, pp. 271–278. Association for Computational Linguistics (2004)
Pang, B., Lee, L.: Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales. In: Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics, pp. 115–124. Association for Computational Linguistics (2005)
Pang, B., Lee, L.: Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval 2(1-2), 1–135 (2008)
Patwardhan, S., Banerjee, S., Pedersen, T.: Using measures of semantic relatedness for word sense disambiguation. In: Gelbukh, A. (ed.) CICLing 2003. LNCS, vol. 2588, pp. 241–257. Springer, Heidelberg (2003)
Pedersen, T., Kolhatkar, V.: WordNet: SenseRelate: AllWords: a broad coverage word sense tagger that maximizes semantic relatedness. In: Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Demonstration Session, pp. 17–20. Association for Computational Linguistics (2009)
Rentoumi, V., Giannakopoulos, G., Karkaletsis, V., Vouros, G.A.: Sentiment analysis of figurative language using a word sense disambiguation approach. In: Proc. of the International Conference RANLP, pp. 370–375 (2009)
Sebastiani, F.: Machine learning in automated text categorization. ACM Computing Surveys (CSUR) 34(1), 1–47 (2002)
Shawe-Taylor, J., Cristianini, N.: Kernel methods for pattern analysis. Cambridge University Press (2004)
Strapparava, C., Mihalcea, R.: Semeval-2007 task 14: Affective text. In: Proceedings of SemEval, vol. 7 (2007)
Suzuki, J., Hirao, T., Sasaki, Y., Maeda, E.: Hierarchical directed acyclic graph kernel: Methods for structured natural language data. In: Proceedings of the 41st Annual Meeting on Association for Computational Linguistics, vol. 1, pp. 32–39. Association for Computational Linguistics (2003)
Täckström, O., McDonald, R.: Discovering fine-grained sentiment with latent variable structured prediction models. In: Clough, P., Foley, C., Gurrin, C., Jones, G.J.F., Kraaij, W., Lee, H., Mudoch, V. (eds.) ECIR 2011. LNCS, vol. 6611, pp. 368–374. Springer, Heidelberg (2011)
Turney, P.D.: 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, pp. 417–424. Association for Computational Linguistics (2002)
Wiegand, M., Klakow, D.: Convolution kernels for opinion holder extraction. In: Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics, pp. 795–803. Association for Computational Linguistics (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Trindade, L.A., Wang, H., Blackburn, W., Rooney, N. (2013). An Enhanced Semantic Tree Kernel for Sentiment Polarity Classification. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2013. Lecture Notes in Computer Science, vol 7817. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37256-8_5
Download citation
DOI: https://doi.org/10.1007/978-3-642-37256-8_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-37255-1
Online ISBN: 978-3-642-37256-8
eBook Packages: Computer ScienceComputer Science (R0)