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Identifying the Opinion Orientation of Online Product Reviews at Feature Level: A Pruning Approach

Identifying the Opinion Orientation of Online Product Reviews at Feature Level: A Pruning Approach

Nilanshi Chauhan, Pardeep Singh
Copyright: © 2017 |Volume: 8 |Issue: 2 |Pages: 20
ISSN: 1947-8186|EISSN: 1947-8194|EISBN13: 9781522513780|DOI: 10.4018/IJISMD.2017040106
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MLA

Chauhan, Nilanshi, and Pardeep Singh. "Identifying the Opinion Orientation of Online Product Reviews at Feature Level: A Pruning Approach." IJISMD vol.8, no.2 2017: pp.92-111. http://doi.org/10.4018/IJISMD.2017040106

APA

Chauhan, N. & Singh, P. (2017). Identifying the Opinion Orientation of Online Product Reviews at Feature Level: A Pruning Approach. International Journal of Information System Modeling and Design (IJISMD), 8(2), 92-111. http://doi.org/10.4018/IJISMD.2017040106

Chicago

Chauhan, Nilanshi, and Pardeep Singh. "Identifying the Opinion Orientation of Online Product Reviews at Feature Level: A Pruning Approach," International Journal of Information System Modeling and Design (IJISMD) 8, no.2: 92-111. http://doi.org/10.4018/IJISMD.2017040106

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Abstract

This article describes how e-commerce has become so vast that almost every product and service can be purchased online, to be delivered at our doorsteps. This has led to a striking increase in the number of online customers. In an attempt to make the online shopping more appealing and transparent to the online customers, the e-retailers allow their customers to express their opinion about the purchased products and services. Recently, analysis of such online reviews has become an active topic of research. This is because it is of immense concern to various stakeholders vs. online merchants, potential customers and the manufacturers of the particular product or service providers. The present article addresses the problem of summarization of such opinions expressed online and aims to create an organized feature-based summary as a solution. The proposed system depends on the frequency of occurrences of the potential features. A number of pruning methods are applied in order to obtain the final feature set and sentiment analysis has been done for each such feature.

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