Asia Pacific Journal of Information Systems (APJIS)

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Current Issue

Date	December 2019
Vol. No.	Vol. 29 No. 4
DOI	https://doi.org/10.14329/apjis.2019.29.4.838
Page	838~855
Title	Multidimensional Analysis of Consumers Opinions from Online Product Reviews
Author	Taewook Kim, Dong Sung Kim, Donghyun Kim, Jong Woo Kim
Keyword	Multidimensional Analysis, Opinion Mining, Product Reviews, Sentiment Analysis
Abstract	Online product reviews are a vital source for companies in that they contain consumers opinions of products. The earlier methods of opinion mining, which involve drawing semantic information from text, have been mostly applied in one dimension. This is not sufficient in itself to elicit reviewers comprehensive views on products. In this paper, we propose a novel approach in opinion mining by projecting online consumers reviews in a multidimensional framework to improve review interpretation of products. First of all, we set up a new framework consisting of six dimensions based on a marketing management theory. To calculate the distances of review sentences and each dimension, we embed words in reviews utilizing Googles pre-trained word2vector model. We classified each sentence of the reviews into the respective dimensions of our new framework. After the classification, we measured the sentiment degrees for each sentence. The results were plotted using a radar graph in which the axes are the dimensions of the framework. We tested the strategy on Amazon product reviews of the iPhone and Galaxy smartphone series with a total of around 21,000 sentences. The results showed that the radar graphs visually reflected several issues associated with the products. The proposed method is not for specific product categories. It can be generally applied for opinion mining on reviews of any product category.

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