Review Analysis of Ride-Sharing Applications Using Machine Learning Approaches Bangladesh Perspective

Abstract

Technology and ride-sharing services have become more accessible and convenient as a result of the growth of the Internet. Passengers increasingly focus on digital reviews to help them make purchasing decisions. Online reviews are incredibly inaccurate, as we have seen time and time again. False reviews were created to deceive customers for commercial purposes. A misleading review might have major repercussions for any organization. Organization is focused on providing good feedback to attract passengers and grow the market. It is possible that a bad review of an app would reduce interest in it. These false reviews endanger the reputation of a product. Because of this, it is critical to have a system in place for detecting fraudulent reviews. This research aims to improve the performance of machine learning models that classify fake reviews. This research aims to contribute to the authenticity of reviews using contemporary techniques and the data from ride-sharing apps. This contribution is vital and significant in a country where ride-sharing apps are becoming more convenient and useful. We created a fresh dataset using different apps-based reviews from the current Bangladesh ride-sharing users’ review section. In this work Decision tree, Random Forest, Gradient Boosting, AdaBoost, and Bi-LSTM machine learning approaches were implemented to get the best performance on our dataset. After creating and running the model, Bidirectional Long Short-Term Memory (Bi-LSTM) achieved 85% best accuracy and 85.0 F1 score with training data rather than other machine learning algorithms.