Abstract
We propose to use semi-supervised learning methods to classify evaluative expressions, that is, tuples of subjects, their attributes, and evaluative words, that indicate either favorable or unfavorable opinions towards a specific subject. Due to its characteristics, the semi-supervised method that we use can classify evaluative expressions in a corpus by their polarities. This can be accomplished starting from a very small set of seed training examples and using contextual information in the sentences to which the expressions belong. Our experimental results with actual Weblog data show that this bootstrapping approach can improve the accuracy of methods for classifying favorable and unfavorable opinions.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up? sentiment classification using machine learning techniques. In: EMNLP 2002, pp. 76–86 (2002)
Dave, K., Lawrence, S., Pennock, D.M.: Mining the peanut gallery: Opinion extraction and semantic classification of product reviews. In: 12th WWW Conference, pp. 519–528 (2003)
Cristianini, N., Shawe-Taylor, J.: An Introduction to Support Vector Machines (and other kernel-based learning methods). Cambridge University Press, Cambridge (2000)
Nigam, K., McCallum, A., Thrun, S., Mitchell, T.: Text classification from labeled and unlabeled documents using EM. Machine Learning 39, 103–134 (2000)
Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society Series B 39, 1–38 (1977)
Hatzivassiloglou, V., McKeown, K.R.: Predicting the semantic orientation of adjectives. In: 35th ACL, pp. 174–181 (1997)
Turney, P.D.: Thumbs up? thumbs down? semantic orientation applied to unsupervised classification of reviews. In: 40th ACL, pp. 417–424 (2002)
Kamps, J., Marx, M., Mokken, R.J., de Rijke, M.: Using wordnet to measure semantic orientations of adjectives. In: 4th LREC, pp. 1115–1118 (2004)
Kim, S.M., Hovy, E.: Determining the sentiment of opinions. In: 20th COLING, pp. 1367–1373 (2004)
Kudo, T., Matsumoto, Y.: A boosting algorithm for classification of semi-structured text. In: EMNLP 2004, pp. 301–308 (2004)
Wilson, T., Wiebe, J., Hwa, R.: Just how mad are you? finding strong and weak opinion clauses. In: 19th AAAI (2004)
Pang, B., Lee, L.: A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts. In: 42nd ACL, pp. 271–278 (2004)
Riloff, E., Wiebe, J.: Learning extraction patterns for subjective expressions. In: EMNLP 2003, pp. 105–112 (2003)
Blum, A., Mitchell, T.: Combining labeled and unlabeled data with co-training. In: Proceedings of the Workshop on Computational Learning Theory, pp. 92–100 (1998)
Hofmann, T., Puzicha, J.: Statistical models for co-occurrence data. Technical Report AIM-1625, Artifical Intelligence Laboratory, Massachusetts Institute of Technology (1998)
Jaakkola, T., Haussler, D.: Exploiting generative models in discriminative classifiers. NIPS 11, 487–493 (1998)
Takamura, H., Okumura, M.: A comparative study on the use of labeled and unlabeled data for large margin classifiers. In: 1st IJCNLP 2004, pp. 620–625 (2004)
Nanno, T., Fujiki, T., Suzuki, Y., Okumura, M.: Automatically collecting, monitoring, and mining japanese weblogs. In: 13th WWW Conference, pp. 320–321 (2004)
Ikehara, S., Miyazaki, M., Shirai, S., Yokoo, A., Nakaiwa, H., Ogura, K., Ooyama, Y., Hayashi, Y.: Goi-Taikei – A Japanese Lexicon. Iwanami Shoten (1997)
Wiebe, J.: Instructions for annotating opinions in newspaper articles. Technical report, University of Pittsburgh Technical Report, TR-02-101 (2002)
Tanaka, Y., Takamura, H., Okumura, M.: Extraction and classification of facemarks with kernel methods. In: IUI 2005, pp. 28–34 (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Suzuki, Y., Takamura, H., Okumura, M. (2006). Application of Semi-supervised Learning to Evaluative Expression Classification. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2006. Lecture Notes in Computer Science, vol 3878. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11671299_52
Download citation
DOI: https://doi.org/10.1007/11671299_52
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-32205-4
Online ISBN: 978-3-540-32206-1
eBook Packages: Computer ScienceComputer Science (R0)