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Reseach Article

Survey of Techniques for Opinion Mining

by Nilesh M. Shelke, Shriniwas Deshpande, Vilas Thakre
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 57 - Number 13
Year of Publication: 2012
Authors: Nilesh M. Shelke, Shriniwas Deshpande, Vilas Thakre
10.5120/9176-3579

Nilesh M. Shelke, Shriniwas Deshpande, Vilas Thakre . Survey of Techniques for Opinion Mining. International Journal of Computer Applications. 57, 13 ( November 2012), 30-35. DOI=10.5120/9176-3579

@article{ 10.5120/9176-3579,
author = { Nilesh M. Shelke, Shriniwas Deshpande, Vilas Thakre },
title = { Survey of Techniques for Opinion Mining },
journal = { International Journal of Computer Applications },
issue_date = { November 2012 },
volume = { 57 },
number = { 13 },
month = { November },
year = { 2012 },
issn = { 0975-8887 },
pages = { 30-35 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume57/number13/9176-3579/ },
doi = { 10.5120/9176-3579 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:00:22.437366+05:30
%A Nilesh M. Shelke
%A Shriniwas Deshpande
%A Vilas Thakre
%T Survey of Techniques for Opinion Mining
%J International Journal of Computer Applications
%@ 0975-8887
%V 57
%N 13
%P 30-35
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Opinion mining refers to computational techniques for analyzing the opinions that are extracted from various sources. Existing research work on Opinion is based upon business and e-commerce such as product reviews and movie ratings. Opinion mining involves computational treatment of opinion and subjectivity in text. It has suddenly attracted the attention of the researcher fraternity. This survey paper describes techniques and approaches that promise to directly enable opinion-oriented information seeking systems. An attempt has been made to discuss in de tails various approaches to perform a computational treatment of sentiments and opinions. Various supervised or data-driven techniques for opinion mining like Naïve Byes, Maximum Entropy, SVM are discussed and their strengths and drawbacks are touched upon.

References
  1. V. Hatzivassiloglou and K. R. McKeown. Predicting the semantic orientation of adjectives. In proceedings of 35th ACL. , 1997.
  2. V. Hatzivassiloglou and J. Wiebe. Effects of adjective orientation and gradability on sentence subjectivity. In proceedings of 18th International Conference on Computational Linguistics. , 2000.
  3. A. Andreevskaia and S. Bergler. "Mining wordnet for a fuzzy sentiment: Sentiment Tag Extraction from Wordnet Glosses". In proceedings of EACL. , 2006.
  4. A. Esilu and F. Sebastini. Sentiwordnet: it is publicly available resource for opinion mining. In proceedings of LREC 2006, 2006.
  5. M. Gamon and A. Aue. Automatic Identification of Sentiment Vacabulary Exploiting Low Association with known sentiment terms. In proceedings of ACL workshop on feature engineering in machine learning in NLP, 2005.
  6. Dave, D. , Lawrence, A. , and Pennock, D. "Mining the Peanut Gallery: Opinion Extraction and Semantic Classification of Product Reviews". Proceedings of International World Wide Web Conference (WWW'03), 2003.
  7. Pang, B. , Lee, L. and Vaithyanathan, S. "Thumbs up? Sentiment Classification Using Machine Learning Techniques". Proceedings of the 2002 Conference on Empirical Methods in Natural Language Processing (EMNLP'02), 2002.
  8. Turney, P. "Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews". ACL'02, 2002.
  9. Kim, S. and Hovy, E. "Determining the Sentiment of Opinions". Proceedings of the 20th International Conference on Computational Linguistics (COLING'04), 2004.
  10. Wiebe, J. and Riloff, E. "Creating Subjective and Objective Sentence Classifiers from Unannotated Texts". Proceedings of International Conference on Intelligent Text Processing and Computational Linguistics (CICLing'05), 2005.
  11. Wilson, T. , Wiebe, J. and Hwa, R. , "Just How Mad Are You? Finding Strong and Weak Opinion Clauses". Proceedings of National Conference on Artificial Intelligence (AAAI'04), 2004.
  12. Hu, M and Liu, B. "Mining and Summarizing Customer Reviews". Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'04), 2004.
  13. Liu, B. , Hu, M. and Cheng, J. "Opinion Observer: Analyzing and Comparing Opinions on the Web", Proceedings of International World Wide Web Conference (WWW'05), 2005.
  14. Pang, Bo and Lee, Lillian and Vaithyanathan, Shivakumar, Thumbs up? Sentiment Classification using Machine Learning Techniques, Proceedings of EMNLP 2002.
  15. Pang, Bo and Lee, Lillian and Vaithyanathan, Shivakumar, Thumbs up?: Sentiment Classification using machine learning techniques, In Proceedings of the ACL-02 conference on Empirical Methods in Natural Language, 2002
  16. David D. Lewis. 1998. "The Independence Assumption in Information Retrieval". In Proc. of the European Conference on Machine Learning (ECML), p. p. 4–15.
  17. Pedro Domingos and Michael J. Pazzani. 1997, "On the Optimality of the Simple Bayesian Classifier Under Zero-One Loss. Machine Learning", 29(2-3):103–130.
  18. Adam L. Berger, Stephen A. Della Pietra, and Vincent J. Della Pietra. 1996. "A Maximum Entropy Approach to Natural Language Processing". Computational Linguistics, 22(1):39–71.
  19. Andrew McCallum and Kamal Nigam. 1998. "A Comparison of Event Models for Naive Bayes Text Classification". In Proc. of the AAAI-98 Workshop on Learning for Text Categorization, pages 41–48.
  20. Stanley Chen and Ronald Rosenfeld. 2000. "A Survey of Smoothing Techniques for ME Models". IEEE Trans. Speech and Audio Processing, 8(1):37–50.
  21. Mihalcea, R. and Liu, H. (2006). "A Corpus-Based Approach to Finding Happiness", in the AAAI Spring Symposium on Computational Approaches to Weblogs, Stanford, California, USA.
  22. Mehrabian, A. (1995). "Framework for a Comprehensive Description and Measurement of Emotional States". Genetic, Social, and General Psychology Monographs, 121, p. p. 339-361.
  23. Alm, C. O. , Roth, D. and Sproat, R. (2005). Emotions from text: machine learning for text based emotion prediction. In Proceedings of the Joint Conference on Human Language Technology / Empirical Methods in Natural Language Processing (HLT/EMNLP 2005),Vancouver, Canada, p. p. 579-586.
  24. Mihalcea, R. and Strapparava, C. (2005). Making Computers Laugh: Investigations in Automatic Humor Recognition, In Proceedings of the Joint Conference on Human Language Technology/Empirical Methods in Natural Language Processing(HLT/EMNLP), p. p. 531-538, Vancouver, Canada.
  25. Ekman, P. (1992). An Argument for Basic Emotions. Cognition and Emotion, p. p. 169-200.
  26. Whitelaw, C. , Garg, N. & Argamon, S. (2005). Using Appraisal groups for sentiment analysis. Proceedings of ACM SIGIR Conference on Information and Knowledge Management (CIKM 2005) p. p. 625 -631.
  27. Kamps, Maarten Marx, Robert J. Mokken and Maarten De Rijke, "Using Wordnet to Measure Semantic Orientation of Adjectives", Proceedings of 4th International Conference on Language Resources and Evaluation, pp. 1115-1118, Lisbon, portugal, 2004.
  28. Andrea Esuli and Fabrizio Sebastiani, "Determining the Semantic Orientation of Terms through Gloss Classification", Proceedings of 14th ACM International Conference on Information and Knowledge Management, p. p. 617-624, Bremen, Germany, 2005.
  29. Chunxu Wu, Lingfeng Shen, "A New Method of Using Contextual Information to Infer the Semantic Orientations of Context Dependent Opinions", 2009 International Conference on Artificial Intelligence and Computational Intelligence.
  30. Ting-Chun Peng and Chia-Chun Shih , "An Unsupervised Snippet-based Sentiment Classification Method for Chinese Unknown Phrases without using Reference Word Pairs", 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology Journal Of Computing, Volume 2 (8), August 2010, ISSN, p. p. 2151-9617 .
  31. Gang Li, Fei Liu, "A Clustering-based Approach on Sentiment Analysis", 2010, 978-1-4244-6793-8/10 ©2010 IEEE.
  32. Chaovalit, Lina Zhou, "Movie Review Mining: a Comparison between Supervised and Unsupervised Classification Approaches", Proceedings of the 38th Hawaii International Conference on System Sciences – 2005.
Index Terms

Computer Science
Information Sciences

Keywords

Polar expression opinion mining POS tagger entropy corpus sentiment emotion machine learning