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AI and Opinion Mining, Part 2
July/August 2010 (vol. 25 no. 4)
pp. 72-79

Opinion mining, a subdiscipline within data mining and computational linguistics, refers to the computational techniques for extracting, classifying, understanding, and assessing the opinions expressed in various online news sources, social media comments, and other user-generated content. This Trends & Controversies department and the previous one include articles on opinion mining from distinguished experts in computer science and information systems. Each article presents a unique innovative research framework, computational methods, and selected results and examples.

1. T. Macer, M. Pearson, and F. Sebastiani, "Cracking the Code: What Customers Say, in their own Words," Proc. 50th Ann. Conf. Market Research Soc. (MRS 07), MRS, 2007.
2. D. Giorgetti and F. Sebastiani, "Automating Survey Coding by Multiclass Text Categorization Techniques," J. Am Soc. Information Science and Technology, vol. 54, no. 14, 2003, pp. 1269–1277.
3. G. Forman, "Quantifying Counts and Costs via Classification," Data Mining and Knowledge Discovery, vol. 17, no. 2, 2008, pp. 164–206.
4. Y. Rubner, C. Tomasi, and L.J. Guibas, "A Metric for Distributions with Applications to Image Databases," Proc. 6th Int'l Conf. Vision (ICCV 98), IEEE CS Press, 1998, pp. 59–66.
5. T. Joachims, "A Support Vector Method for Multivariate Performance Measures," Proc. 22nd Int'l Conf. Machine Learning (ICML 05), ACM Press, 2005, pp. 377–384.
1. E. Riloff, J. Wiebe, and T. Wilson, "Learning Subjective Nouns using Extraction Pattern Bootstrapping," Proc. 7th Conf. Natural Language Learning, ACM Press, 2003, pp. 25–32.
2. A. Abbasi et al., "Affect Analysis of Web Forums and Blogs using Correlation Ensembles," IEEE Trans. Knowledge and Data Eng., vol. 20, no. 9, 2008, pp. 1168–1180.
3. E. Riloff, S. Patwardhan, and J. Wiebe, "Feature Subsumption for Opinion Analysis," Proc. Conf. Empirical Methods in Natural Language Processing, ACM Press, 2006, pp. 440–448.
4. A. Abbasi and H. Chen, "CyberGate: A Design Framework and System for Text Analysis of Computer Mediated Communication," MIS Quarterly, vol. 32, no. 4, 2008, pp. 811–837.
5. Y. Dang, Y. Zhang, and H. Chen, "A Lexicon-Enhanced Method for Sentiment Classification: An Experiment on Online Product Reviews," IEEE Intelligent Systems, vol. 25, no. 4, pp. 46–53.
6. A. Esuli and F. Sebastiani, "SentiWordNet: A Publicly Available Lexical Resource for Opinion Mining," Proc. 5th Conf. Language Resources and Evaluation (LREC), European Assoc. Language Resources, 2006, pp. 417–422.
7. T. Mullen and N. Collier, "Sentiment Analysis Using Support Vector Machines with Diverse Information Sources," Proc. Conf. Empirical Methods in Natural Language Processing, ACM Press, 2004, pp. 412–418.
8. A. Abbasi et al., "Selecting Attributes for Sentiment Classification using Feature Relation Networks," to be published in IEEE Trans. Knowledge and Data Eng., 2010; http://www.sba.uwm.edu/abbasi_a/index_files IEEETKDE_FRN.pdf.
9. X.B. Xue and Z.H. Zhou, "Distributional Features for Text Categorization," IEEE Trans. Knowledge and Data Eng., vol. 21, no. 3, 2009, pp. 428–444.

Index Terms:
opinion mining, sentiment analysis, sentiment quantification, sentiment classification, intelligent systems, intelligent feature selection, Epinions, Edmunds, Rotten Tomatoes
Citation:
"AI and Opinion Mining, Part 2," IEEE Intelligent Systems, vol. 25, no. 4, pp. 72-79, July-Aug. 2010, doi:10.1109/MIS.2010.94
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