Abstract
Automatically tracking attitudes, feelings and reactions in on-line forums, blogs and news is a desirable instrument to support statistical analyses by companies, the government, and even individuals. In this paper, we present a novel approach to polarity classification of short text snippets, which takes into account the way data are naturally distributed into several topics in order to obtain better classification models for polarity. Our approach is multi-step, where in the initial step a standard topic classifier is learned from the data and the topic labels, and in the ensuing step several polarity classifiers, one per topic, are learned from the data and the polarity labels. We empirically show that our approach improves classification accuracy over a real-world dataset by over 10%, when compared against a standard single-step approach using the same feature sets. The approach is applicable whenever training material is available for building both topic and polarity learning models.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Gindl, S., Liegl, J.: Evaluation of different sentiment detection methods for polarity classification on web-based reviews. In: Proceedings of the18th European Conference on Artificial Intelligence (ECAI 2008), ECAI Workshop on Computational Aspects of Affectual and Emotional Interaction, Patras, Greece (2008)
Abbasi, A., Chen, H., Salem, A.: Sentiment analysis in multiple languages: Feature selection for opinion classification in web forums. ACM Trans. Inf. Syst. 26(3) (2008)
Boiy, E., Hens, P., Deschacht, K., Moens, M.: Automatic sentiment analysis in on-line text. In: Chan, L., Martens, B. (eds.) ELPUB, pp. 349–360 (2007)
Gamon, M.: Sentiment classification on customer feedback data: noisy data, large feature vectors, and the role of linguistic analysis. In: COLING 2004: Proceedings of the 20th international conference on Computational Linguistics, Morristown, NJ, USA, p. 841. Association for Computational Linguistics (2004)
Niu, Y., Zhu, X., Li, J., Hirst, G.: Analysis of polarity information in medical text. In: AMIA Annu. Symp. Proc., pp. 570–574 (2005)
Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up? Sentiment classification using machine learning techniques. In: Proceedings of the 2002 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 79–86 (2002)
Hatzivassiloglou, V., McKeown, K.: Predicting the semantic orientation of adjectives. In: Proceedings of the eighth conference on European chapter of the Association for Computational Linguistics, Morristown, NJ, USA, pp. 174–181. Association for Computational Linguistics (1997)
Koller, D., Sahami, M.: Hierarchically classifying documents using very few words. In: Fisher, D. (ed.) ICML, pp. 170–178. Morgan Kaufmann, San Francisco (1997)
Dumais, S., Chen, H.: Hierarchical classification of web content. In: SIGIR, pp. 256–263 (2000)
Pang, B., Lee, L.: A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts. In: Proceedings of the ACL, pp. 271–278 (2004)
Niu, Y., Zhu, X., Hirst, G.: Using outcome polarity in sentence extraction for medical question-answering. In: AMIA Annu. Symp. Proc., pp. 599–603 (2006)
Esuli, A., Sebastiani, F.: Sentiwordnet: A publicly available lexical resource for opinion mining. In: Proceedings of the 5th Conference on Language Resources and Evaluation (LREC 2006), pp. 417–422 (2006)
Efron, M.: Cultural orientation: Classifying subjective documents by cocitation analysis. In: Proceedings of the 2004 AAAI Fall Symposium on Style and Meaning in Language, Art, Music, and Design, pp. 41–48 (2004)
Agrawal, R., Rajagopalan, S., Srikant, R., Xu, Y.: Mining newsgroups using networks arising from social behavior. In: WWW 2003: Proceedings of the 12th international conference on World Wide Web, pp. 529–535. ACM, New York (2003)
Yu, H., V.H.: Towards answering opinion questions: separating facts from opinions and identifying the polarity of opinion sentences. In: Proceedings of the 2003 conference on Empirical methods in natural language processing, Morristown, NJ, USA, pp. 129–136. Association for Computational Linguistics (2003)
Mccallum, A., Nigam, K.: A comparison of event models for naive bayes text classification. In: AAAI Workshop on Learning for Text Categorization (1998)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Xia, L., Gentile, A.L., Munro, J., Iria, J. (2009). Improving Patient Opinion Mining through Multi-step Classification. In: Matoušek, V., Mautner, P. (eds) Text, Speech and Dialogue. TSD 2009. Lecture Notes in Computer Science(), vol 5729. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04208-9_13
Download citation
DOI: https://doi.org/10.1007/978-3-642-04208-9_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04207-2
Online ISBN: 978-3-642-04208-9
eBook Packages: Computer ScienceComputer Science (R0)