Sentiment Analysis Of Shopee Application User Reviews Using Naïve Bayes Classifier

Adelia Nopiazinda Br Panjaitan; Ismail Husein

Authors

Adelia Nopiazinda Br Panjaitan Mathematics, Universitas Islam Negeri Sumatera Utara Medan, Tuntungan, Medan, Indonesia
Ismail Husein Mathematics, Universitas Islam Negeri Sumatera Utara Medan, Tuntungan, Medan, Indonesia

Keywords:

Naïve Bayes Classifier, Shope Application, User Reviews, Sentiment Analysis

Abstract

Shopee is currently the number one online shopping application in Indonesia. The purpose of the study is to find out the classification of shopee application user reviews and the results of prediction accuracy by classifying naïve bayes classifiers. Positive reviews, negative reviews, total of all reviews, and review status are the variables of the study. Simple random sampling technique to determine the sample, the results were selected by Indonesian user reviews. Scraping the Google Play Store website page generates attribute variable data, which is then determined using rating, date, and review criteria. To create a naïve bayes classifier, the before and after probabilities are calculated using rapid miner software. The results showed that naïve bayes classifier obtained an accuracy score with a 50% training data trial and a 50% data test resulting in an accuracy of 90.53%, a precision of 90.53%, and a recall of 100% with an average accuracy of 90.53%. It can be concluded that naïve bayes classifier can be used in predicting the review rate of Shopee application users effectively and efficiently

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