Sentiment Analysis from User-Generated Reviews of Ride-Sharing Mobile Applications

Abstract

Smartphone applications play an increasingly significant part in our everyday lives, and their use has skyrocketed. The Google Play Store is a well-known plat-form through which one may obtain various Android applications whereas ap-plication like Ridesharing play a significant role in delivering public services more efficiently and effectively, as seen by the widespread adoption of many different types of innovative applications. This study focuses on users' reactions to these ridesharing applications, and it employs sentiment analysis to extract emotions from text reviews posted on the Google App Store platform given by the users. The primary goal is to examine the perspectives of customers and users of these applications. A total of 1818 data was gathered from the Google Play Store and divided into three categories: positive, negative, and neutral. The model was evaluated using the CNN, LSTM, and DistilBERT algorithms, with DistilBERT outperforming the others and achieving the highest accuracy of 98.84 %.