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A Modified Metaheuristic Algorithm for Opinion Mining

by K. Saraswathi, A. Tamilarasi
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 58 - Number 11
Year of Publication: 2012
Authors: K. Saraswathi, A. Tamilarasi
10.5120/9329-3634

K. Saraswathi, A. Tamilarasi . A Modified Metaheuristic Algorithm for Opinion Mining. International Journal of Computer Applications. 58, 11 ( November 2012), 43-47. DOI=10.5120/9329-3634

@article{ 10.5120/9329-3634,
author = { K. Saraswathi, A. Tamilarasi },
title = { A Modified Metaheuristic Algorithm for Opinion Mining },
journal = { International Journal of Computer Applications },
issue_date = { November 2012 },
volume = { 58 },
number = { 11 },
month = { November },
year = { 2012 },
issn = { 0975-8887 },
pages = { 43-47 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume58/number11/9329-3634/ },
doi = { 10.5120/9329-3634 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:02:13.749825+05:30
%A K. Saraswathi
%A A. Tamilarasi
%T A Modified Metaheuristic Algorithm for Opinion Mining
%J International Journal of Computer Applications
%@ 0975-8887
%V 58
%N 11
%P 43-47
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Opinion mining is a recent discipline combining Information Retrieval and Computational Linguistics which is concerned with the opinion a document expresses and not just with the topic in the document. Online forums, newsgroups, blogs, and specialized sites provide voluminous information feeds from where opinions can be retrieved. Opinion's polarity is established through application of machine learning techniques for classification of textual reviews as either a positive or negative class. In this paper, it is proposed to extract the feature set from reviews using Inverse document frequency and the reviews are classified as positive or negative using Bagging algorithms. The proposed method is evaluated using a subset of Internet Movie Database (IMBd).

References
  1. Comscore and Kelsey, http://www. shop. org/c/journal_articles/view_article_content?groupId=1&articleId702&version=1. 0.
  2. Popescu and O. Etzioni, Extracting product features and opinions from reviews, Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing. , (2005), pp. 339-346.
  3. M. Hu and B. Liu, Mining and Summarizing Customer Reviews, Proceedings of the 10th ACM International Conference on Knowledge Discovery and Data Mining (SIGKDD-2004), 8 (2004), pp. 168–174.
  4. B. He, C. Macdonald, J. He, and I. Ounis, An Effective Statistical Approach to Blog Post Opinion Retrieval, CIKM. , 10 (2008), pp. 1063-1069.
  5. M. Hu and B. Liu, Mining Opinion Features in Customer Reviews, Proceedings of the 19th National Conference on Artificial Intelligence. , 7 (2004), pp. 755-760.
  6. S. Kim, P. Pantel, T. Chklovski, and M. Pennacchiotti, Automatically Assessing Review Helpfulness, EMNLP. , 7 (2006), pp. 423-430.
  7. B. Liu, M. Hu, and J. Cheng, Opinion Observer: Analyzing and Comparing Opinions,WWW. , 5 (2005), pp. 342-351.
  8. B. Liu, Sentiment Analysis and Subjectivity, to appear in Handbook of Natural Language Processing, Second Edition, 2010.
  9. Ana-Maria Popescu and Oren Etzioni. Extracting product features and opinions from reviews. In Proceedings of EMNLP 2005, pp. 339-346.
  10. Michael Gamon, Anthony Aue, Simon Corston-Oliver and Eric Ringger. 2005. Pulse: Mining customer opinions from free text. In Proceedings of IDA 2005, pp. 121-132.
  11. Minqing Hu and Bing Liu. Mining and summarizing customer reviews. In Proceedings of ACM-KDD 2004, p. 168-177.
  12. Bing Liu, Minqing Hu and Junsheng Cheng. Opinion Observer: Analyzing and comparing opinions on the web. In Proceedings of WWW 2005, pp. 342-351.
  13. Bo Pang and Lillian Lee. Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales. In Proceedings of ACL 2005, pp. 115-124.
  14. Whitelaw, Casey, Navendu Garg, and Shlomo Argamon. 2005. Using appraisal groups for sentiment analysis. In Proceedings of the 14th ACM International Conference on Information and Knowledge Management (CIKM-2005), pages 625–631.
  15. Kennedy, Alistair and Diana Inkpen. 2006. Sentiment classi?cation of movie reviews using contextual valence shifters. Computational Intelligence, 22(2):110–125.
  16. Tony Mullen and Nigel Collier. Sentiment analysis using support vector machines with diverse information sources. In Proceedings of EMNLP 2004, pp. 412-418.
  17. Bo Pang and Lillian Lee. A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts. In Proceedings of ACL 2004, pp. 271-278.
  18. Bo Pang, Lillian Lee and Shivakumar Vaithyanathan. Thumbs up? Sentiment classification using machine learning techniques. In Proceedings of EMNLP 2002, pp. 79-86.
  19. Domingos P, Pazzani M (1997) On the optimality of the simple Bayesian classifier under zero-one loss. Mach Learn 29:103–130
  20. Breiman L, Friedman JH, Olshen RA, Stone CJ (1984) Classification and regression trees. Wadsworth, Belmont
  21. Breiman, L. : Bagging predictors. Machine Learning 24, 123–140 (1996).
Index Terms

Computer Science
Information Sciences

Keywords

Opinion mining Sentiment analysis Movie reviews Naive Bayes CART Bagging

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