CFP last date
22 April 2024
Reseach Article

Sentiment Classification and Feature based Summarization of Movie Reviews in Mobile Environment

by Savita Harer, Sandeep Kadam
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 100 - Number 1
Year of Publication: 2014
Authors: Savita Harer, Sandeep Kadam
10.5120/17490-8008

Savita Harer, Sandeep Kadam . Sentiment Classification and Feature based Summarization of Movie Reviews in Mobile Environment. International Journal of Computer Applications. 100, 1 ( August 2014), 30-35. DOI=10.5120/17490-8008

@article{ 10.5120/17490-8008,
author = { Savita Harer, Sandeep Kadam },
title = { Sentiment Classification and Feature based Summarization of Movie Reviews in Mobile Environment },
journal = { International Journal of Computer Applications },
issue_date = { August 2014 },
volume = { 100 },
number = { 1 },
month = { August },
year = { 2014 },
issn = { 0975-8887 },
pages = { 30-35 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume100/number1/17490-8008/ },
doi = { 10.5120/17490-8008 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:28:49.197444+05:30
%A Savita Harer
%A Sandeep Kadam
%T Sentiment Classification and Feature based Summarization of Movie Reviews in Mobile Environment
%J International Journal of Computer Applications
%@ 0975-8887
%V 100
%N 1
%P 30-35
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

A new framework is designed for sentiment classification and feature based summarization system in a mobile environment. Posting online reviews has become an increasingly popular way for people to share their opinions about specific product or service with other users. It has become a common practice for web technologies to provide the venues and facilities for people to publish their reviews. Sentiment classification and feature based summarization are essential steps for the classification and summarization of movie reviews. System proposed Random forest method for sentiment classification of movie reviews. Identification of movie features and opinion words are both important for feature based summarization. System identified movie features using a novel approach called Latent Semantic Analysis (LSA) and frequency based approach. Then system identified opinion words using part-of-speech (POS) tagging method. The result of LSA is extended to LSA based filtering mechanism to reduce the size of review summary. System design focused on the sentiment classification accuracy and system response time.

References
  1. Chien-Liang Liu, Wen-Hoar Hsaio, Chia-Hoang Lee, Gen-Chi Lu, and Emery Jou, "Movie Rating and Review Summarization in Mobile Environment", IEEE VOL. 42, NO. 3, MAY 2012.
  2. L. Zhuang, F. Jing, and X. -Y. Zhu, "Movie review mining and summarization," in Proc. 15th ACM Int. Conf. Inf. Knowl. Manage. 2006, pp. 43–50.
  3. Pang, Bo and Lee, Lillian and Vaithyanathan, Shivakumar, Thumbs up? Sentiment Classification using Machine Learning Techniques, Proceedings of EMNLP 2002.
  4. 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.
  5. (2001), LIBSVM: A library for support vector machines[online]. Available:http://www. csie. ntu. edu. tw/cjlin/libsvm.
  6. Mark Hall, Eibe Frank, Geoffrey Holmes, Bernhard Pfahringer, Peter Reutemann, and Ian H. Witten. The weka data mining software: an update. SIGKDD Explor. Newsl, 11(1): pp. 10–18, November 2009.
  7. Savita Harer and Sandeep kadam, "Mining and Summarizing Movie Reviews in Mobile Environment," in International Journal of Computer Science and Information Technologies, ISSN: 0975-9646, Vol. 5 (3), 2014.
  8. Andrew L. Maas and Raymond E. Daly and Peter T. Pham and Dan Huang and Andrew Y. and Christopher Potts. Learning Word Vectors for Sentiment Analysis. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: HumanLanguage Technologies. , pages 142–150, Portland, Oregon, USA, June 2011. ACL.
  9. T. Hofmann, "Probabilistic Latent Semantic Indexing," Proc. 22ndAnn. Int'l ACM SIGIR Conf. Research and Development in InformationRetrieval (SIGIR), pp. 50-57, 1999.
  10. T. Hofmann, "Unsupervised learning by probabilistic latent semantic analysis,"Mach. Learn. , vol. 42, no. 1/2, pp. 177–196, 2001.
  11. T. Joachims, Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms. Norwell, MA: Kluwer, 2002.
  12. T. Mullen and N. Collier, "Sentiment analysis using support vector machines with diverse information sources," in Proc. EMNLP, 2004, pp. 412–418.
  13. T. K. Landauer, P. W. Foltz, and D. Laham, "Introduction to latent semantic analysis," Discourse Processes, vol. 25, pp. 259–284, 1998.
  14. M. Hu and B. Liu, "Mining and summarizing customer reviews," in Proc. 10th ACMSIGKDD Int. Conf. Knowl. Discov. DataMining, 2004, pp. 168–177.
  15. Savita Harer and Yogesh Sayaji, "A Survey On Sentiment Analysis for Movie Domain in Mobile Environment," in International journal of Computer Networking, Wireless and Mobile Communications, ISSN: 2278-9448 , ISSN: 2250-1568,2014.
  16. A. Esuli and F. Sebastiani, "SENTIWORDNET: A publicly available lexical resource for opinion mining," in Proc. 5th Conf. Lang. Res. Eval. , 2006, pp. 417–422.
Index Terms

Computer Science
Information Sciences

Keywords

Movie reviews Sentiment Classification Summarization POS tagging.