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Comparative Evaluation of Supervised Learning Algorithms for Sentiment Analysis of Movie Reviews

by Raj K. Palkar, Kewal D. Gala, Meet M. Shah, Jay N. Shah
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 142 - Number 1
Year of Publication: 2016
Authors: Raj K. Palkar, Kewal D. Gala, Meet M. Shah, Jay N. Shah
10.5120/ijca2016909660

Raj K. Palkar, Kewal D. Gala, Meet M. Shah, Jay N. Shah . Comparative Evaluation of Supervised Learning Algorithms for Sentiment Analysis of Movie Reviews. International Journal of Computer Applications. 142, 1 ( May 2016), 20-26. DOI=10.5120/ijca2016909660

@article{ 10.5120/ijca2016909660,
author = { Raj K. Palkar, Kewal D. Gala, Meet M. Shah, Jay N. Shah },
title = { Comparative Evaluation of Supervised Learning Algorithms for Sentiment Analysis of Movie Reviews },
journal = { International Journal of Computer Applications },
issue_date = { May 2016 },
volume = { 142 },
number = { 1 },
month = { May },
year = { 2016 },
issn = { 0975-8887 },
pages = { 20-26 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume142/number1/24861-2016909660/ },
doi = { 10.5120/ijca2016909660 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:43:46.629930+05:30
%A Raj K. Palkar
%A Kewal D. Gala
%A Meet M. Shah
%A Jay N. Shah
%T Comparative Evaluation of Supervised Learning Algorithms for Sentiment Analysis of Movie Reviews
%J International Journal of Computer Applications
%@ 0975-8887
%V 142
%N 1
%P 20-26
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Online forums and social networking websites provide users with a platform for expressing their opinions. Manually evaluating these reviews for crucial analytical information is cumbersome. Sentiment analysis deals with analyzing such massively available textual data and determining its polarity. This research paper provides a comparative study of multiple wellknown supervised machine learning algorithms on three standard datasets confined to the domain of movie reviews. The study is supported by illustrative plots and experimental results. The research work can be used as a base for further exploration in predicting the sentiment value of textual data in alternate domains using advanced machine learning algorithms.

References
  1. Pang, B., Lee, L.: “Opinion Mining and Sentiment Analysis”, in “Foundations and Trends in Information Retrieval”, Volume 2, Issue 12, January 2008, pp. 1135.
  2. P.Walia, Marisha, V.K.Singh, and M.K,Singh, "Evaluating Machine Learning and Unsupervised Semantic Orientation Approaches for Sentiment Analysis of Textual Reviews", 2012 IEEE International Conference on Computational Intelligence and Computing Research.
  3. B.Ripley. tree: Classification and Regression Trees, 2012. URL http://CRAN.Rproject.org/package=tree. R package version 1.031. [p7]
  4. T.P.Jurka. maxent: An R package for lowmemory multinomial logistic regression with support for semiautomated text classification. The R Journal, 4(1): 5659, June 2012. [p7]
  5. D. Meyer, E. Dimitriadou, K. Hornik, A. Weingessel, and F. Leisch. e1071: Misc Functions of the Department of Statistics (e1071), TU Wien, 2012. URL http://CRAN.Rproject.org/package=e1071. R package version 1.61. [p7]
  6. A. Liaw and M. Weiner, Classification and regression by randomForest. R News, 2(3): 1822, 2002. [p7].
  7. F. Neri, C. Aliprandi, F. Capeci, M. Cuadros and T. By, "Sentiment Analysis on Social Media," Advances in Social Networks Analysis and Mining (ASONAM), 2012 IEEE/ACM International Conference, Istanbul, 2012, pp. 919926.
  8. J.Akaichi, Z. Dhouioui, M. J. LopezHuertas Perez, “Text mining facebook status updates for sentiment classification”, 2013 17th IEEE International Conference on System Theory, Control and Computing (ICSTCC), pp. 640645.
  9. Nagamma P, Pruthvi H. R., Nisha K. K., Shwetha N. H., “An Improved Sentiment Analysis Of Online Move Reviews Based On Clustering For BoxOffice Prediction”, 2015 IEEE International Conference on Computing, Communication and Automation (ICCCA2015), pp. 933937.
  10. Eduard H. Hovy, “What are Sentiment, Affect, and Emotion? Applying the Methodology of Michael Zock to Sentiment Analysis”,Book Part 1, 2015, pp. 1324.
  11. T. P. Jurka et al., “RTextTools: A Supervised Learning Package for Text Classification”, The R Journal Vol. 5/1, June 2013, pp. 612.
  12. Maas et al., “Learning Word Vectors for Sentiment Analysis”, in Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies. Association for Computational Linguistics, June 2011, pp. 142150.
  13. Bo Pang and Lillian Lee, “A Sentimental Education: Sentiment Analysis Using Subjectivity Summarization Based on Minimum Cuts”, in Proceedings of the ACL, 2004.
  14. Kotzias et al., ‘From Group to Individual Labels using Deep Features’, KDD 2015.
  15. Anna Jurek, Yaxin Bi, Maurice Mulvenna, “Twitter Sentiment Analysis for SecurityRelated Information Gathering”, 2014 IEEE Joint Intelligence and Security Informatics Conference.
  16. Sagar Bhuta, Avit Doshi, Uchit Doshi and Meera Narvekar,”A Review of Techniques for Sentiment Analysis Of Twitter Data”, 2014 International Conference on Issues and Challenges in Intelligent Computing Techniques (ICICT).
Index Terms

Computer Science
Information Sciences

Keywords

Sentiment Analysis Machine Learning Text classification Naïve Bayes Support Vector Machine Maximum Entropy Classification and Regression Trees Random Forest movie reviews.

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