Opinion Classification from Online Reviews based on Support Vector Machine

Authors(2) :-Sangram Ashok Patil, Prof. K. B. Manwade

With the rapid growth of internet, a huge number of product reviews are evolved up on the Web. From these reviews, customers can get direct assessments of product information and direct supervision of their purchase actions. Meanwhile, manufacturers can obtain immediate advice and opportunities to improve the quality of their products in a timely fashion. Any customers who buy a product can place their opinion about product features. New customers who want to buy a product he reads the reviews of the previous customers. So reviews are helps to new customers to decide to buy a product or not. Product manufacturer are also use the reviews of customer for product development. Opinions of customers are sentiments about product. Opinions given by customers are not in specific format and it does not have any syntax to define it. Reviews may be in single word or may be in one sentence or it may be in multiline format. And every customer has different opinions about product so that new customer need to read all the reviews this process is difficult and time consuming process. Here we have implement a system for online reviews classification based on polarity by using support vector machine and provide a review based rating system. From these online reviews we also find opinion target and opinion word using word alignment model and shows the topical relation.

Authors and Affiliations

Sangram Ashok Patil
ME (CSE ) Student,Ashokrao Mane Group of Institutons,Vathar, Kolhapur, Maharashtra, India
Prof. K. B. Manwade
Asst. Prof., Ashokrao Mane Group of Institutions, Vathar, Kolhapur, Maharashtra, India

Feature Extraction, Opinion target and word, Text Classification

  1. Kang Lui, Liheng Xu, and Jun Zhao, "CoExtracting Opinion Targets and Opinion Words from Online Reviews Based on the Word Alignment Model", IEEE Transactions on Knowledge and Data Engineering, Vol.27 No.3 March 2015
  2. Ding et al., Xiaowen Ding, Bing Liu, and Philip S. Yu, "A holistic lexicon-based approach to opinion mining", In Proceedings of the Conference on Web Search and Web Data Mining (WSDM), 2008.
  3. Fangtao Li, Chao Han, Minlie Huang, Xi-aoyan Zhu, Yingju Xia, Shu Zhang, and HaoYu,"Structure-aware review mining and summarization", In Chu-Ren Huang and Dan Jurafsky, editors, COLING, pages 653–661. Tsinghua University Press, 2010.
  4. Wei Jin and Hay Ho Huang, "A novel lexicalized hmm-based learning framework for web opinion mining", In Proceedings of International Conference on Machine Learning (ICML), 2009.
  5. Mingqin Hu and Bing Liu, "Mining opinion features in customer reviews", In Proceedings of Conference on Artificial Intelligence (AAAI), 2004.
  6. Guang Qiu, Bing Liu, Jiajun Bu, and Chun Che, "Expanding domain sentiment lexicon through double propagation", 2009.
  7. Ahmad Abbasi, Stephen France, zhu zhang, and Hisinchun Chen, "Selecting Attributes for Sentiment Classification using Feature Relational Network", IEEE Transactions on Knowledge and Data Engineering. Vol 23. No 3. Page 447-462. 2011
  8. Loubes, J. M. and van de Geer, S "Support vector machines and the Bayes rule in classification", Data mining knowledge and discovery 6 259-275.2002
  9. Gongde Guo, Hui Wang, David Bell, Yaxin Bi and Kieran Greer, "KNN Model-Based Approach in Classification", Proc. ODBASE pp- 986 – 996, 2003

Publication Details

Published in : Volume 2 | Issue 4 | July-August 2016
Date of Publication : 2016-08-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 250-254
Manuscript Number : IJSRST162457
Publisher : Technoscience Academy

Print ISSN : 2395-6011, Online ISSN : 2395-602X

Cite This Article :

Sangram Ashok Patil, Prof. K. B. Manwade, " Opinion Classification from Online Reviews based on Support Vector Machine", International Journal of Scientific Research in Science and Technology(IJSRST), Print ISSN : 2395-6011, Online ISSN : 2395-602X, Volume 2, Issue 4, pp.250-254, July-August-2016.
Journal URL : http://ijsrst.com/IJSRST162457

Article Preview