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 (National Institute of Technology, Hamirpur, India) and Pardeep Singh (National Institute of Technology, Hamirpur, India)
Copyright: © 2017 |Pages: 20
DOI: 10.4018/IJISMD.2017040106
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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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This study makes use of supervised machine learning approach to achieve the objectives of feature-level opinion mining. Opinion summarization uses machines learning algorithms which can be broadly classified into three categories namely supervised, semi-supervised and unsupervised. Supervised machine learning algorithms require a labeled training data for constructing an inference function. This labeled training dataset is very time consuming to construct or acquire. Semi-supervised learning algorithms use a large unlabeled dataset along with a small labeled training dataset. The unsupervised machine learning algorithms do not require such labeled training datasets. Since the data given to the unsupervised learning algorithms is in unlabeled, it is difficult to evaluate the accuracy of the output produced.

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