Sentiment Analysis Website of Online Hotel Booking Application Reviews Using the Naive Bayes Algorithm

Abstract

Today, there are many applications available on the Google Play Store, especially the online hotel booking application. In Indonesia, 2 out of 3 people book hotels online and users also rely on digital reviews for travel inspiration as well as research and bookings. Users can find out user satisfaction by looking at reviews from previous users, but it is very problematic if we read the reviews of this application one by one because it takes a very long time. Measuring the level of user satisfaction of an application can be done by knowing how the sentiment from the public. This paper provides an approach to analyzing sentiments for online hotel booking applications based on user reviews on the Google Play Store using the Naive Bayes algorithm. The process starts with data collection using web-scraping, text preprocessing using python, data labeling using SentiStrength, classification with the Naive Bayes algorithm, and website development using Django Web Framework. This website provides information support for users in choosing an online hotel booking application. From this study, the highest accuracy value obtained was 94%.