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Applied Computational Intelligence and Soft Computing
Volume 2014 (2014), Article ID 735942, 9 pages
http://dx.doi.org/10.1155/2014/735942
Research Article

Opinion Mining from Online User Reviews Using Fuzzy Linguistic Hedges

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

Received 29 August 2013; Accepted 1 January 2014; Published 20 February 2014

Academic Editor: Sebastian Ventura

Copyright © 2014 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.

Abstract

Nowadays, there are several websites that allow customers to buy and post reviews of purchased products, which results in incremental accumulation of a lot of reviews written in natural language. Moreover, conversance with E-commerce and social media has raised the level of sophistication of online shoppers and it is common practice for them to compare competing brands of products before making a purchase. Prevailing factors such as availability of online reviews and raised end-user expectations have motivated the development of opinion mining systems that can automatically classify and summarize users’ reviews. This paper proposes an opinion mining system that can be used for both binary and fine-grained sentiment classifications of user reviews. Feature-based sentiment classification is a multistep process that involves preprocessing to remove noise, extraction of features and corresponding descriptors, and tagging their polarity. The proposed technique extends the feature-based classification approach to incorporate the effect of various linguistic hedges by using fuzzy functions to emulate the effect of modifiers, concentrators, and dilators. Empirical studies indicate that the proposed system can perform reliable sentiment classification at various levels of granularity with high average accuracy of 89% for binary classification and 86% for fine-grained classification.