Naive Bayes Classifier Method on Sentiment Analysis of Bibit Application Users in Play Store

Abstract

The increasing public interest in investment and supported by technological advances has begun to appear investment applications in the community which aim to facilitate the public in making investments. One of the investment applications that is widely used today is the Bibit application. This application is widely used by novice investors because of its ease of opening accounts, disbursing funds, purchasing mutual funds and easy-to-understand application design. Because investment applications are still new to the community, there are still many people who doubt and worry about the quality of the Bibit application, marked by the number of reviews in the review column available on the play store. Reviews on the application become a forum for criticism and suggestions to the application and become one of the considerations for potential users. Because reviews can be positive or negative towards the Seedling application. Sentiment analysis is needed to analyze whether the sentiment tends to be positive or negative. Then, classification is carried out to obtain a classification model that can be used to predict user sentiment using the Naive Bayes Classifier method. The results obtained obtained seed application users tend to have positive sentiments with an accuracy value of 79.45%.