Abstract
In this paper, we observe the effects that discourse function attribute to the task of training learned classifiers for sentiment analysis. Experimental results from our study show that training on a corpus of primarily persuasive documents can have a negative effect on the performance of supervised sentiment classification. In addition we demonstrate that through use of the Multinomial Naïve Bayes classifier we can minimise the detrimental effects of discourse function during sentiment analysis.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Liu, B.: Sentiment Analysis and Subjectivity. Handbook of Natural Language Processing 2, 568 (2010)
Kinneavy, J.E.: The Basic Aims of Discourse. College Composition and Communication 20, 297–304 (1969)
Kinneavy, J.L.: A Theory of Discourse: The Aims of Discourse. Norton (1971)
Shannon, C.E.: A Mathematical Theory of Communication. Bell Systems Technical Journal 27, 379–423 (1948)
Ortony, A., Clore, G.L., Collins, A.: The Cognitive Structure of Emotions. Cambridge University Press, Cambridge (1988)
Smith, P., Lee, M.: A CCG-based Approach to Fine-Grained Sentiment Analysis. In: Proceedings of the 2nd Workshop on Sentiment Analysis where AI meets Psychology, The COLING 2012 Organizing Committee, pp. 3–16 (2012)
Mullen, T., Collier, N.: Sentiment Analysis using Support Vector Machines with Diverse Information Sources. In: Lin, D., Wu, D. (eds.) Proceedings of EMNLP 2004, pp. 412–418. Association for Computational Linguistics (2004)
Bloom, K., Garg, N., Argamon, S.: Extracting Appraisal Expressions. In: Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics; Proceedings of the Main Conference, Association for Computational Linguistics, pp. 308–315 (2007)
Dermouche, M., Khouas, L., Velcin, J., Loudcher, S.: AMI&ERIC: How to Learn with Naive Bayes and Prior Knowledge: An Application to Sentiment Analysis. In: Second Joint Conference on Lexical and Computational Semantics (*SEM), Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013), vol. 2, pp. 364–368. Association for Computational Linguistics (2013)
Miller, G.R.: 1. In: The Persuasion Handbook: Developments in Theory and Practice, pp.3–17. Sage (2002)
Hunston, S.: Corpus Approaches to Evaluation. Routledge (2011)
Smith, P., Lee, M.: Cross-discourse Development of Supervised Sentiment Analysis in the Clinical Domain. In: Proceedings of the 3rd Workshop in Computational Approaches to Subjectivity and Sentiment Analysis, pp. 79–83. Association for Computational Linguistics (2012)
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E.: Scikit-learn: Machine learning in Python. Journal of Machine Learning Research 12, 2825–2830 (2011)
Bird, S., Loper, E., Klein, E.: Natural Language Processing with Python. O’Reilly Media Inc. (2009)
Guerini, M., Strapparava, C., Stock, O.: Resources for Persuasion. In: Proceedings of LREC 2008, European Language Resources Association, pp. 235–242 (2008)
Greene, S., Resnik, P.: More than Words: Syntactic Packaging and Implicit Sentiment. In: Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, pp. 503–511. Association for Computational Linguistics (2009)
McCallum, A., Nigam, K., et al.: A Comparison of Event Models for Naive Bayes Text Classification. In: AAAI 1998 Workshop on Learning for Text Categorization, vol. 752, pp. 41–48. AAAI (1998)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Smith, P., Lee, M. (2014). Acknowledging Discourse Function for Sentiment Analysis. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2014. Lecture Notes in Computer Science, vol 8404. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-54903-8_4
Download citation
DOI: https://doi.org/10.1007/978-3-642-54903-8_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-54902-1
Online ISBN: 978-3-642-54903-8
eBook Packages: Computer ScienceComputer Science (R0)