APJIS Asia Pacific Journal of Information Systems

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The Journal for Information Professionals

Asia Pacific Journal of Information Systems (APJIS), a Scopus and ABDC indexed journal, is a
flagship journal of the information systems (IS) field in the Asia Pacific region.

ISSN 2288-5404 (Print) / ISSN 2288-6818 (Online)

Editor : Hee-Woong Kim

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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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