Naïve Bayes Algorithm for Sentiment Analysis of Blibli.com Review on Google Play Store

Abstract

Currently, there are approximately 354 million active mobile phones in Indonesia, placing the country fourth globally in terms of the highest number of mobile phone users. E-Commerce, as a form of online transaction, enables the digital exchange of goods and services to meet daily needs. This research aims to implement sentiment analysis using the Naive Bayes classification algorithm as a method to gather user opinions. Thus, the study not only provides insights into customer satisfaction with Blibli.com but also serves as a basis for potential improvements in services or feature development to enhance the online shopping experience. Overall, the Naive Bayes algorithm successfully achieved an accuracy of around 84%, demonstrating its proficiency in categorizing sentiment in reviews. When focusing on negative data, the Naive Bayes algorithm exhibited a precision of approximately 79%, recall of around 95%, and an f1-score of about 86%, indicating its success in identifying and classifying negative reviews with high precision and sensitivity. On the positive side, the Naive Bayes algorithm achieved a precision of about 91%, recall of around 83%, and an f1-score of about 87%.