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A Study on Classifications of Useful Customer Reviews by Applying Text Mining Approach
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 Title & Authors
A Study on Classifications of Useful Customer Reviews by Applying Text Mining Approach
Lee, Hong Joo;
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Customer reviews are one of the important sources for purchase decision makings in online stores. Online stores have tried to provide useful reviews in product pages to customers. To assess the usefulness of customer reviews before other users have voted enough on the reviews, diverse aspects of reviews were utilized in prevous studies. Style and semantic information were utilized in many studies. This study aims to test diverse alogrithms and datasets for identifying a proper classification method and threshold to classify useful reviews. In particular, most researches utilized ratio type helpfulness index as used. However, there is another type of usefulness index utilized in or, count type helpfulness index. There was no proper threshold to classify useful reviews yet for count type helpfulness index. This study used reivews and their usefulness votes on restaurnats from to devise diverse datasets and applied text mining approaches to classify useful reviews. Random Forest, SVM, and GLMNET showed the greater values of accuracy than other approaches.
Customer Review;Classification;Usefulness index;
 Cited by
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