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http://dx.doi.org/10.6109/jkiice.2018.22.9.1153

Evaluation of Classification Algorithm Performance of Sentiment Analysis Using Entropy Score  

Park, Man-Hee (Department of Business Administration, Catholic University of Pusan)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.22, no.9, 2018 , pp. 1153-1158 More about this Journal
Abstract
Online customer evaluations and social media information among a variety of information sources are critical for businesses as it influences the customer's decision making. There are limitations on the time and money that the survey will ask to identify a variety of customers' needs and complaints. The customer review data at online shopping malls provide the ideal data sources for analyzing customer sentiment about their products. In this study, we collected product reviews data on the smartphone of Samsung and Apple from Amazon. We applied five classification algorithms which are used as representative sentiment analysis techniques in previous studies. The five algorithms are based on support vector machines, bagging, random forest, classification or regression tree and maximum entropy. In this study, we proposed entropy score which can comprehensively evaluate the performance of classification algorithm. As a result of evaluating five algorithms using an entropy score, the SVMs algorithm's entropy score was ranked highest.
Keywords
Entropy score; sentiment analysis; classification algorithm; performance evaluation;
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