Abstract
Traditional text categorization is usually a topic-based task, but a subtle demand on information retrieval is to distinguish between positive and negative view on text topic. In this paper, a new method is explored to solve this problem. Firstly, a batch of Concerned Concepts in the researched domain is predefined. Secondly, the special knowledge representing the positive or negative context of these concepts within sentences is built up. At last, an evaluating function based on the knowledge is defined for sentiment classification of free text. We introduce some linguistic knowledge in these procedures to make our method effective. As a result, the new method proves better compared with SVM when experimenting on Chinese texts about a certain topic.
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
Hearst, M.A.: Direction-based text interpretation as an information access refinement. In: Jacobs, P. (ed.) Text-Based Intelligent Systems: Current Research and Practice in Information Extraction and Retrieval. Lawrence Erlbaum Associates, Mahwah (1992)
Biber, D.: Variation across Speech and Writing. Cambridge University Press, Cambridge (1988)
Hatzivassiloglou, V., McKeown, K.: Predicting the semantic orientation of adjectives. In: Proc. of the 35th ACL/8th EACL, pp. 174–181 (1997)
Turney, P.D., Littman, M.L.: Unsupervised learning of semantic orientation from a hundred-billion-word corpus. Technical Report EGB-1094, National Research Council Canada (2002)
Hearst, M.: Direction-based text interpretation as an information access refinement. In: Jacobs, P. (ed.) Text-Based Intelligent Systems. Lawrence Erlbaum Associates, Mahwah (1992)
Pang, B., Lee, L.: A Sentimental Education: Sentiment Analysis Using Subjectivity Summarization Based on Minimum Cuts. In: Proceedings of the 42nd ACL, pp. 271–278 (2004)
Das, S., Chen, M.: Yahoo! for Amazon: Extracting market sentiment from stock message boards. In: Proc. of the 8th Asia Pacific Finance Association Annual Conference (2001)
Hatzivassiloglou, V., Wiebe, J.: Effects of Adjective Orientation and Gradability on Sentence Subjectivity. In: COLING, pp. 299–305 (2000)
Turney, P.: Thumbs up or thumbs down? Semantic orientation applied to unsupervised classication of reviews. In: Proc. of the ACL (2002)
Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up? Sentiment Classification using Machine Learning Techniques. In: Proc. Conf. on EMNLP (2002)
Sack, W.: On the computation of point of view. In: Proc. of the Twelfth AAAI, Student abstract p. 1488 (1994)
Tong, R.M.: An operational system for detecting and tracking opinions in on-line discussion. In: Workshop note, SIGIR Workshop on Operational Text Classification (2001)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Hu, Y., Duan, J., Chen, X., Pei, B., Lu, R. (2005). A New Method for Sentiment Classification in Text Retrieval. In: Dale, R., Wong, KF., Su, J., Kwong, O.Y. (eds) Natural Language Processing – IJCNLP 2005. IJCNLP 2005. Lecture Notes in Computer Science(), vol 3651. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11562214_1
Download citation
DOI: https://doi.org/10.1007/11562214_1
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29172-5
Online ISBN: 978-3-540-31724-1
eBook Packages: Computer ScienceComputer Science (R0)