Opinion Mining using Hybrid Methods

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IJCA Proceedings on International Conference on Innovations in Computing Techniques (ICICT 2015)
© 2015 by IJCA Journal
ICICT 2015 - Number 2
Year of Publication: 2015
Authors:
K. Umamaheswari
S. P. Rajamohana
G. Aishwaryalakshmi

K.umamaheswari, S.p.rajamohana and G.aishwaryalakshmi. Article: Opinion Mining using Hybrid Methods. IJCA Proceedings on International Conference on Innovations in Computing Techniques (ICICT 2015) ICICT 2015(2):17-21, July 2015. Full text available. BibTeX

@article{key:article,
	author = {K.umamaheswari and S.p.rajamohana and G.aishwaryalakshmi},
	title = {Article: Opinion Mining using Hybrid Methods},
	journal = {IJCA Proceedings on International Conference on Innovations in Computing Techniques (ICICT 2015)},
	year = {2015},
	volume = {ICICT 2015},
	number = {2},
	pages = {17-21},
	month = {July},
	note = {Full text available}
}

Abstract

Opinion mining is opinion of the public that is given by each user about a particular product. People post many comments and messages about a movie posted in these social network. The comments of each user will be taken as opinions for each movie posted in these web forums. In this paper the rating of movie in twitter is taken to review a movie by using opinion mining This paper proposed a hybrid methodsusing SVM and PSO to classify the user opinions as positive, negative for the movie review dataset which could be used for better decisions.

References

  • X. Yu, Y. Liu,X. Huang and A. An, "Mining online Reviews for Predicting Sales Performance", IEEE Transaction on Knowledge and Data Engineering, vol. 24, No. 4, pp. 720-734, Apr 2012.
  • A. Pak and P. paroubek,"Twitter as a Corpus for Sentiment Analysis and Opinion Mining",Proceedings of the seventh international conference on language resources and evaluation (LREC' 10),pp. 1320-1326,nov 2010.
  • Moontae lee, Patrick Grafe, "Multiclass Sentimental Analysis with Restaurant Reviews",Department of Computer Science, Stanford University,June 2010.
  • B. Liu, Web Data Mining, Second Edi. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011.
  • S. F. Pratama, A. K. Muda, Y. H. Choo and N. A. Muda, "PSO and Computational Inexpensive Sequential Forward Floating Selection in Acquiring Significant Features for Handwritten Authorship," in 2011 11th International Conference on Hybrid Intelligent Systems (HIS), 2011, pp. 358-363.
  • S. L. Salzberg. "On Comparing Classifiers: Pitfalls to Avoid and a Recommended Approach",Data Mining and Knowledge Discovery, vol. 1, pp. 317 -328, 1997. .
  • Ethem Alpaydin. 2004. Introduction to Machine Learning(Adaptive Computation andMachine Learning). The MIT Press. Hayter Anthony J. 2007. Probability and Statistics for Engineersand Scientists. Duxbury, Belmont, CA, USA. Kushal Dave, Steve Lawrence, and David M. Pennock. 2003.
  • Twitter sentiment analysis. Final Projects from CS224N for Spring 2008/2009 at The Stanford Natural Language Processing Group. ,2009
  • J. Bollen, H. Mao, and X. Zeng ," twitter mood predicts stock market ",Journal of Computational Science, vol. 2, no. 1, pp. 1-8, Mar. 2011. [10. ] x. yu y. liu ,x. haung and A,AN ," mining online reviews for predicting sales performance", IEEE Transactions on Knowledge and Data Engineering, vol. 24, no. 4, pp. 720-734, Apr. 2012.
  • Umamaheswari K. , Sumathi S. , Aparna V. and Arthi A. , 'Text Classification using Enhanced Naïve Bayes with Genetic algorithm', International Journal of Computer Applications in Engineering, Technology and Sciences, Gujarat, Vol. 1, No. 2, pp. 263-270, 2009.