The Community for Technology Leaders
RSS Icon
Subscribe
Issue No.04 - July-Aug. (2012 vol.27)
pp: 37-44
Zhongwu Zhai , Tsinghua University
Bing Liu , University of Illinois at Chicago
Jingyuan Wang , Tsinghua University
Hua Xu , Tsinghua University
Peifa Jia , Tsinghua University
ABSTRACT
A constrained semisupervised learning method classifies words and phrases into feature groups, making it easier to produce an opinion summary of various product reviews.
INDEX TERMS
Feature extraction, Context awareness, Information analysis, Semisupervised learning, Data mining, Bayesian methods, semi-supervised learning, opinion mining, product feature grouping
CITATION
Zhongwu Zhai, Bing Liu, Jingyuan Wang, Hua Xu, Peifa Jia, "Product Feature Grouping for Opinion Mining", IEEE Intelligent Systems, vol.27, no. 4, pp. 37-44, July-Aug. 2012, doi:10.1109/MIS.2011.38
REFERENCES
1. M. Hu, and B. Liu, “Mining and Summarizing Customer Reviews,” Proc. ACM Int'l Conf. Knowledge Discovery and Data Mining (SIGKDD 04), ACM, 2004, pp. 168–177.
2. A.-M. Popescu and O. Etzioni, “Extracting Product Features and Opinions from Reviews,” Proc. Conf. Empirical Methods in Natural Language Processing (EMNLP 05), Assoc. for Computational Linguistics, 2005, pp. 339–346.
3. B. Pang and L. Lee, “Opinion Mining and Sentiment Analysis,” Foundations and Trends in Information Retrieval, vol. 2, nos. 1–2, 2008, pp. 1–135.
4. B. Liu, “Sentiment Analysis: A Multifaceted Problem,” IEEE Intelligent Systems, vol. 25, no. 3, 2010, pp. 76–80.
5. K. Nigam et al., “Text Classification from Labeled and Unlabeled Documents Using EM,” Machine Learning, vol. 39, no. 2, 2000, pp. 103–134.
6. G. Carenini, R. Ng, and E. Zwart, “Extracting Knowledge from Evaluative Text,” Proc. 3rd Int'l Conf. Knowledge Capture (K-CAP 05), ACM, 2005, pp. 11–18.
7. B. Liu, M. Hu, and J. Cheng, “Opinion Observer: Analyzing and Comparing Opinions on the Web,” Proc. 14th Int'l Conf. World Wide Web (WWW 05), ACM, 2005, pp. 342–351.
8. L. Lee, “Measures of Distributional Similarity,” Proc. 37th Ann. Meeting Assoc. Computational Linguistics, Assoc. for Computational Linguistics, 1999, pp. 25–32.
9. H. Guo et al., “Product Feature Categorization with Multilevel Latent Semantic Association,” Proc. 18th ACM Conf. Information and Knowledge Management (CIKM 09), ACM, 2009, pp. 1087–1096.
10. D. Blei, A.Y. Ng, and M.I. Jordan, “Latent Dirichlet Allocation,” J. Machine Learning Research, vol. 3, 2003, pp. 993–1022.
11. D. Andrzejewski, X. Zhu, and M. Craven, “Incorporating Domain Knowledge into Topic Modeling via Dirichlet Forest Priors,” Proc. 26th Int'l Conf. Machine Learning (ICML 09), Omnipress, 2009, pp. 25–32.
12. J. Jiang and D. Conrath, “Semantic Similarity Based on Corpus Statistics and Lexical Taxonomy,” Proc. Int'l Conf. Research on Computational Linguistics (ROCLING X); http://arxiv.org/pdf/cmp-lg9709008.pdf.
13. P. Resnik, “Using Information Content to Evaluate Semantic Similarity in a Taxonomy,” Proc. 14th Int'l Joint Conf. Artificial Intelligence (IJCAI 95), AAAI, 1995, pp. 448–453.
14. D. Lin, “An Information-Theoretic Definition of Similarity,” Proc. 15th Int'l Conf. Machine Learning (ICML 98), Omnipress, 1998, pp. 296–304.
15. M. Steyvers and T. Griffiths, “Probabilistic Topic Models,” Handbook of Latent Semantic Analysis, T. Landauer et al., eds, Laurence Erlbaum, 2007, pp. 424–440.
20 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool