Abstract
Everyday many online product sales websites and specialized reviewing forums publish a massive volume of human-generated product reviews. People use these reviews as valuable free source of knowledge when decide to buy products. Therefore, an accurate automated system for distinguishing useful reviews from non-useful ones is of great importance. This article presents a new model for specifying the usefulness of comments using the textual features extracted from the reviews. Various types of features including emotion-related, linguistic and text-related features, valence, arousal, and dominance (VAD) values, review-length and polarity of comments are exploited in this study. Moreover, two new algorithms are presented: an improved evidential algorithm for emotion recognition, and an algorithm for extracting VAD values for each review. Finally, the usefulness of reviews is predicted using the mentioned features and an improved Dempster-Shafer score fusion algorithm. The proposed method is applied to review datasets of Books and Video Games of Amazon. The results show that combining the features associated with emotions, features of VAD, and text-related features improves the accuracy of predicting the usefulness of reviews. Also, in comparison with the original Dempster-Shafer method, the precision of the improved Dempster-Shafer algorithm for both datasets is 15% and 11% higher, respectively.
Highlights
With the advent of the Web and the expansion of e-commerce, users are expressing their views on products and services on many specialized and commercial sites to interact and work together
The results of this study showed that negative online reviews are more useful than positive ones
This paper presents a model for predicting review usefulness
Summary
With the advent of the Web and the expansion of e-commerce, users are expressing their views on products and services on many specialized and commercial sites to interact and work together. Customers share their personal beliefs, experiences of purchasing decisions, and evaluations towards services or products [1]. These reviews contain valuable information and can be used to analyze people’s attitudes and interests. They can be used to identify and analyze people’s positive and negative views on a variety of targets such as locations, products, and specific events.
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