Opinion digger

Published 26 October 2010

Samaneh Moghaddam, Martin Ester

Citations194

TL;DR

An unsupervised method is proposed, called Opinion Digger, which extracts important aspects of a product and determines the overall consumer's satisfaction for each, by estimating a rating in the range from 1 to 5, and is demonstrated on a real life dataset that was crawled from Epinions.com.

Abstract

Mining customer reviews (opinion mining) has emerged as an interesting new research direction. Most of the reviewing websites such as Epinions.com provide some additional information on top of the review text and overall rating, including a set of predefined aspects and their ratings, and a rating guideline which shows the intended interpretation of the numerical ratings. However, the existing methods have ignored this additional information. We claim that using this information, which is freely available, along with the review text can effectively improve the accuracy of opinion mining. We propose an unsupervised method, called Opinion Digger, which extracts important aspects of a product and determines the overall consumer's satisfaction for each, by estimating a rating in the range from 1 to 5. We demonstrate the improved effectiveness of our methods on a real life dataset that we crawled from Epinions.com.

Keywords

Computer ScienceSocial Sciences

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