Abstract
Before deciding to buy a product, many people tend to consult others’ opinions on it. Web provides a perfect platform which one can get information to find out the advantages and disadvantages of the product of his interest. How to automatically manage the numerous opinionated documents and then to give suggestions to the potential customers is becoming a research hotspot recently. Constructing a sentiment resource is one of the vital elements of opinion finding and polarity analysis tasks. For a specific domain, the sentiment resource can be regarded as a dictionary, which contains a list of product feature words and several opinion words with sentiment polarity for each feature word. This paper proposes an automatic algorithm to extraction feature words and opinion words for the sentiment resource. We mine the feature words and opinion words from the comments on the Web with both NLP technique and statistical method. Left context entropy is proposed to extract unknown feature words; Adjective rules and background corpus are taken into consideration in the algorithm. Experimental results show the effectiveness of the proposed automatic sentiment resource construction approach. The proposed method that combines NLP and statistical techniques is better than using only NLP-based technique. Although the experiment is built on mobile telephone comments in Chinese, the algorithm is domain independent.
Supported by the Chinese National Key Foundation Research & Development Plan (2004CB318108), Natural Science Foundation (60621062, 60503064, 60736044) and National 863 High Technology Project (2006AA01Z141).
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
HowNet, http://www.keenage.com/
Ye, Q., Shi, W., Li, Y.: Sentiment Classification for Movie Reviews in Chinese by Improved Semantic Oriented Approach. In: Proceedings of the 39th Annual Hawaii international Conference on System Sciences, HICSS, January 04 - 07, vol. 03, p. 53.2. IEEE Computer Society, Washington (2006)
Li, J., Sun, M.: Experimental Study on Sentiment Classification of Chinese Review using Machine Learning Techniques. In: Proceedings of IEEE International Conference on Natural Language Processing and Knowledge Engineering 2007, pp. 393–400 (2007)
Hu, M., Liu, B.: Mining Opinion Features in Customer Reviews. In: Proceedings of Nineteenth National Conference on Artificial Intelligence, San Jose, California, USA, July 2-29, pp. 755–760. AAAI Press, Menlo Park (2004)
Hu, M., Liu, B.: Mining and summarizing customer reviews. In: Proceedings of the Tenth ACM SIGKDD international Conference on Knowledge Discovery and Data Mining, KDD 2004, Seattle, WA, USA, August 22 - 25, pp. 168–177. ACM, New York (2004)
Liu, B., Hu, M., Cheng, J.: Opinion observer: analyzing and comparing opinions on the Web. In: Proceedings of the 14th international Conference on World Wide Web, WWW 2005, Chiba, Japan, May 10-14, pp. 342–351. ACM, New York (2005)
Popescu, A., Etzioni, O.: Extracting product features and opinions from reviews. In: Proceedings of the Conference on Human Language Technology and Empirical Methods in Natural Language Processing, Human Language Technology Conference. Association for Computational Linguistics, Vancouver, British Columbia, Canada, October 06-08, pp. 339–346. Morristown, NJ (2005)
Yi, J., Niblack, W.: Sentiment Mining in WebFountain. In: Proceedings of the 21st international Conference on Data Engineering (Icde 2005), ICDE, April 05-08, vol. 00, pp. 1073–1083. IEEE Computer Society, Washington (2005)
Scaffidi, C., Bierhoff, K., Chang, E., Felker, M., Ng, H., Jin, C.: Red Opal: product-feature scoring from reviews. In: Proceedings of the 8th ACM Conference on Electronic Commerce, EC 2007, San Diego, California, USA, June 11-15, pp. 182–191. ACM, New York (2007)
Wang, B., Wang, H.: Bootstrapping both Product Properties and Opinion Words from Chinese Reviews with Cross-Training. In: Proceedings of the IEEE/WIC/ACM international Conference on Web intelligence, Web Intelligence, November 02 - 05, pp. 259–262. IEEE Computer Society, Washington (2007)
Su, Q., Xu, X., Guo, H., Guo, Z., Wu, X., Zhang, X., Swen, B., Su, Z.: Hidden sentiment association in chinese Web opinion mining. In: Proceeding of the 17th international Conference on World Wide Web, WWW 2008, Beijing, China, April 21 - 25, pp. 959–968. ACM, New York (2008)
ICTCLAS, http://www.nlp.org.cn/
Sogou Lab Internet Vocabulary, http://www.sogou.com/labs/dl/w.html
Luo, Z., Song, R.: An Integrated Method for Chinese Unknown Word Extraction. In: Proceedings of 3rd ACL SIGHAN Workshop on Chinese Language Processing, Barcelona, Spain, pp. 148–155 (2004)
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
Li, Z., Zhang, M., Ma, S., Zhou, B., Sun, Y. (2009). Automatic Extraction for Product Feature Words from Comments on the Web. In: Lee, G.G., et al. Information Retrieval Technology. AIRS 2009. Lecture Notes in Computer Science, vol 5839. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04769-5_10
Download citation
DOI: https://doi.org/10.1007/978-3-642-04769-5_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04768-8
Online ISBN: 978-3-642-04769-5
eBook Packages: Computer ScienceComputer Science (R0)