Mining comparative opinions from customer reviews for Competitive Intelligence

Decision Support Systems

Volumn 50, Issue 4, 2011, Pages 743-754

  Xu, Kaiquan ^a   Liao, Stephen Shaoyi ^a   Li, Jiexun ^b   Song, Yuxia ^a  

^a CITY UNIVERSITY OF HONG KONG   (Hong Kong)

^b Drexel University   (United States)

Author keywords

Competitive Intelligence; Enterprise risk management; Opinion mining

Indexed keywords

CUSTOMER REVIEW; DECISION SUPPORTS; ENTERPRISE RISK MANAGEMENT; GRAPHICAL MODEL; INFORMATION SOURCES; KEY FACTORS; MARKETING STRATEGY; NEW PRODUCT; NEW SOURCES; OPINION MINING; POTENTIAL RISKS; PRODUCT REVIEWS; WEB 2.0;

COMPETITIVE INTELLIGENCE; CUSTOMER SATISFACTION; DECISION SUPPORT SYSTEMS; INDUSTRY; RISK ANALYSIS; RISK MANAGEMENT; SALES;

COMPETITION;

EID: 79151480152     PISSN: 01679236     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.dss.2010.08.021     Document Type: Article

References (48)

1. A. Abbasi, H. Chen, and A. Salem Sentiment analysis in multiple languages: feature selection for opinion classification in Web forums ACM Transactions on Information Systems 26 3 2008
2. S. Argamon, C. Whitelaw, P. Chase, S. Raj Hota, N. Garg, and S. Levitan Stylistic text classification using functional lexical features Journal of the American Society for Information Science and Technology 58 6 2007
3. S. Bethard, H. Yu, A. Thornton, V. Hatzivassiloglou, and D. Jurafsky Automatic extraction of opinion propositions and their holders Y. Qu, J. Shanahan, J. Wiebe, Proceedings of the AAAI Spring Symposium on Exploring Attitude and Affect in Text: Theories and Applications 2004 Stanford University USA 22 29
4. R.C. Bunescu, and R.J. Mooney A shortest path dependency kernel for relation extraction R.J. Mooney, Proceedings of the Conference on Human Language Technology and Empirical Methods in Natural Language Processing, Vancouver, British Columbia, Canada 2005 724 731
5. R. Bunescu, and R. Mooney Subsequence kernels for relation extraction Y. Weiss, B. Schlkopf, J. Platt, Advances in Neural Information Processing Systems 18 2006 The MIT Press
6. M. Chau, and J. Xu Mining communities and their relationships in blogs: a study of online hate groups International Journal of Human-Computer Studies 65 1 2007
7. H. Chen Intelligence and security informatics: information systems perspective Decision Support Systems 41 3 2006
8. A. Culotta, and J. Sorensen Dependency tree kernels for relation extraction D. Scott, Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics, Barcelona Spain 2004 423
9. A. Divoli, and T.K. Attwood BioIE: extracting informative sentences from the biomedical literature Bioinformatics 21 2005
10. D. Edwards Introduction to Graphical Modelling 2nd ed. 2000 Springer Verlag
11. A. Esuli, and F. Sebastiani SENTIWORDNET: a publicly available lexical resource for opinion mining N. Calzolari, Proceedings of the 5th Conference on Language Resources and Evaluation, Genoa, Italy 2006 1 6
12. Z. Fei extended SentimentWord http://www.jkx.fudan.edu.cn/mcwil/~zcfei/ sent 2009
13. B.A. Hagedorn, M. Ciaramita, and J. Atserias World knowledge in broad-coverage information filtering W. Kraaij, A.P. Vries, Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, The Netherlands, Amsterdam 2007 801 802
14. J. Hakenberg, C. Plake, U. Leser, and H. Kirsch LLL'05 challenge: genic interaction extraction-identification of language patterns based on alignment and finite state automata S. Dzeroski, Proceedings of the 22nd International Conference on Machine Learning, Bonn, Germany 2005 38 45
15. V. Hatzivassiloglou, and J.M. Wiebe Effects of adjective orientation and gradability on sentence subjectivity M. Kay, Proceedings of the 18th Conference on Computational Linguistics, Saarbrücken, Germany 2000 299 305
16. C.W. Hsu, and C.J. Lin A comparison of methods for multiclass support vector machines IEEE Transactions on Neural Networks 13 2 2002
17. M. Hu, and B. Liu Mining and summarizing customer reviews W. Kim, R. Kohavi, Proceedings of the tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Seattle, WA, USA 2004 168 177
18. N. Jindal, and B. Liu Mining comparative sentences and relations K.D. Forbus, Proceedings of 21st National Conference on Artificial Intelligence, Boston, Massachusetts, USA 2006 1 6
19. T. Joachims Text categorization with support vector machines: learning with many relevant features C. Nedellec, C. Rouveirol, Proceedings of the 10th European Conference on Machine Learning, Chemnitz, Germany 1998 137 142
20. T. Joachims SVM-multiclass: Multi-Class Support Vector Machine http://svmlight.joachims.org 2009
21. N. Kambhatla Combining lexical, syntactic, and semantic features with maximum entropy models for extracting relations J.S. Chang, Y.C. Chang, Proceedings of the ACL 2004 on Interactive Poster and Demonstration Sessions, Barcelona Spain 2004 178 181
22. Y. Kim, Y. Jung, and S. Myaeng Identifying opinion holders in opinion text from online newspapers T.Y. Lin, R. Yager, J. Mendel, Proceedings of the 2007 IEEE International Conference on Granular Computing, San Jose, California, USA 2007 699 702
23. L. Kong, Y. Fu, X. Zhou, Q. Liu, and Z. Cui Study on Competitive Intelligence system based on Web Y. Fu, Proceedings of the Workshop on Intelligent Information Technology Application, Zhang Jiajia, China 2007 339 342
24. T. Kudo, and Y. Matsumoto A boosting algorithm for classification of semi-structured text D. Lin, D. Wu, Proceedings of the 2004 Conference on Empirical Methods in Natural Language Processing, Barcelona, Spain 2004 1 8
25. J.D. Lafferty, A. McCallum, and F.C.N. Pereira Conditional random fields: probabilistic models for segmenting and labeling sequence data C.E. Brodley, A.P. Danyluk, Proceedings of the Eighteenth International Conference on Machine Learning, San Francisco, CA, USA 2001 282 289
26. J. Li, Z. Zhang, X. Li, and H. Chen Kernel-based learning for biomedical relation extraction Journal of the American Society for Information Science and Technology 59 5 2008
27. B. Liu Web Data Mining-Exploring Hyperlinks, Contents and Usage Data 2006 Springer Verlag
28. B. Liu, M. Hu, and J. Cheng Opinion observer: analyzing and comparing opinions on the Web A. Ellis, T. Hagino, Proceedings of the 14th International World Wide Web Conference, Chiba, Japan 2005 342 351
29. K. Murphy Conditional Random Field (CRF) Toolbox for Matlab http://www.cs.ubc.ca/~murphyk/Software/CRF/crf.html 2004
30. M.N. Nguyen, and J.C. Rajapakse Multi-class support vector machines for protein secondary structure prediction Genome Informatics 14 2003
31. B. Pang, and L. Lee Opinion mining and sentiment analysis J. Callan, F. Sebastiani, Foundations and Trends in Information Retrieval 2 2008 Now Publishers
32. B. Pang, L. Lee, and S. Vaithyanathan Thumbs up? Sentiment classification using machine learning techniques D. Yarowsky, D. Radev, Proceedings of the Conference on Empirical Methods in Natural Language Processing, Philadelphia, PA, USA 2002 79 86
33. M.E. Porter Competitive Strategy: Techniques for Analyzing Industries and Competitors 1980 Free Press
34. T.S. Raghu, and H. Chen Cyberinfrastructure for homeland security: advances in information sharing, data mining, and collaboration systems Decision Support Systems 43 4 2007
35. E. Riloff, J. Wiebe, and W. Phillips Exploiting subjectivity classification to improve information extraction M.V. Manuela, K. Subbarao, Proceedings of the 20th National Conference on Artificial Intelligence, Pittsburgh, Pennsylvania, USA 2005 1106 1111
36. B. Rosario, and M.A. Hearst Classifying semantic relations in bioscience texts D. Scott, Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics, Barcelona, Spain 2004 430 437
37. University of Sheffield, Natural Language Processing Research Group, GATE, A general architecture for text engineering, available at http://gate.ac.uk, (2009).
38. Stanford Natural Language Processing Group, The Stanford Parser: a statistical parser, http://nlp.stanford.edu/software/lex-parser.shtml (2009).
39. C. Sutton, and A. McCallum An introduction to conditional random fields for relational learning L. Getoor, B. Taskar, Introduction to Statistical Relational Learning, Ch.1 2007 MIT Press
40. P.D. Turney Thumbs up or thumbs down?: semantic orientation applied to unsupervised classification of reviews P. Isabelle, Proceedings of the 40th Annual Meeting on Association for Computational Linguistics, Philadelphia, Pennsylvania, USA 2001 417 424
41. J. Weston, and C. Watkins Multi-class Support Vector Machines, Technical Report May 1998
42. J.M. Wiebe, R.F. Bruce, and T.P. O'Hara Development and use of a gold-standard data set for subjectivity classifications R. Dale, K. Church, Proceedings of the 37th Annual Meeting of the Association for Computational Linguistics on Computational Linguistics, College Park, Maryland, USA 1999 246 253
43. J. Wiebe, T. Wilson, R. Bruce, M. Bell, and M. Martin Learning subjective language Computational Linguistics 30 3 2004
44. WordNet, http://wordnet.princeton.edu, (2009).
45. J. Yi, T. Nasukawa, R. Bunescu, and W. Niblack Sentiment analyzer: extracting sentiments about a given topic using natural language processing techniques J. Shavlik, Proceedings of the Third IEEE International Conference on Data Mining, Melbourne, Florida, USA 2003 427 434
46. J. Yi, and W. Niblack Sentiment mining in WebFountain K. Aberer, M. Franklin, S. Nishio, Proceedings of the 21st International Conference on Data Engineering, Tokyo, Japan 2005 1073 1083
47. H. Yu, and V. Hatzivassiloglou Towards answering opinion questions: separating facts from opinions and identifying the polarity of opinion sentences M. Collins, M. Steedman, Proceedings of the 2003 Conference on Empirical Methods in Natural Language Processing, Sapporo, Japan 2003 129 136
48. D. Zelenko, C. Aone, and A. Richardella Kernel methods for relation extraction Journal of Machine Learning Research 3 2003