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Applied Computational Intelligence and Soft Computing
Volume 2013 (2013), Article ID 910706, 8 pages
Research Article

Semisupervised Learning Based Opinion Summarization and Classification for Online Product Reviews

1Information Technology Department, Sarvajanik College of Engineering & Technology, Surat 395001, India
2Computer Engineering Department, S. V. National Institute of Technology, Surat 395007, India

Received 23 March 2013; Revised 25 June 2013; Accepted 27 June 2013

Academic Editor: Sebastian Ventura

Copyright © 2013 Mita K. Dalal and Mukesh A. Zaveri. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


The growth of E-commerce has led to the invention of several websites that market and sell products as well as allow users to post reviews. It is typical for an online buyer to refer to these reviews before making a buying decision. Hence, automatic summarization of users’ reviews has a great commercial significance. However, since the product reviews are written by nonexperts in an unstructured, natural language text, the task of summarizing them is challenging. This paper presents a semisupervised approach for mining online user reviews to generate comparative feature-based statistical summaries that can guide a user in making an online purchase. It includes various phases like preprocessing and feature extraction and pruning followed by feature-based opinion summarization and overall opinion sentiment classification. Empirical studies indicate that the approach used in the paper can identify opinionated sentences from blog reviews with a high average precision of 91% and can classify the polarity of the reviews with a good average accuracy of 86%.