Sentiment Analysis of Product Reviews as A Customer Recommendation Using the Naive Bayes Classifier Algorithm

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Published: Sep 1, 2019
DOI: https://doi.org/10.47738/ijiis.v2i2.13
Keywords:
Naive bayes classifier, K-means, Sentiment analysis

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Taqwa Hariguna
Amikom University Purwokerto, Indonesia
Wiga Maulana Baihaqi
Amikom University Purwokerto, Indonesia
Aulia Nurwanti
Amikom University Purwokerto, Indonesia

Abstract

In an e-commerce Shopee, the process of selling and buying continues to run every day, and the comments given by consumers will increase more and more. Comments given by consumers will be the reference/review of a product that has been purchased by consumers. Consumers freely provide a review containing positive comments and negative comments in the Comments field listed on the Shopee e-commerce website. With the above problems, researchers will do a research with the method of sentiment analysis to distinguish classes in product review comments that include positive comment class or negative comment class using a combination of K-means and naive Bayes classifier. K-means used to determine the grouping of classes; naive Bayes classifier used to get the value of accuracy. The results obtained based on clustering K-means include getting 116 negative comments on product reviews and 37 negative comments product reviews. Accuracy results obtained from product review comment data of 77.12%. Thus, the accuracy value using K-means and naive Bayes classifier without manual data get a higher accuracy value is compared using K-means, Naive Bayes classifier, and manual data get results lower accuracy of 56.86%. From the results above the most comments is a negative comment of 116 data review comments product, from the results of the study can be concluded that one of the products of Spatuafa named high heels women know the Ribbon Ikat FX18 the condition of the product is not good enough due to the high negative comments compared to positive comments

Article Details

Issue
Vol. 2 No. 2: September 2019
Section
Articles

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