Review Analysis of Ride-sharing Application Using BILSTM Based RNN Model- 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've 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. Providing good feedback to attract passengers and grow the market. It's 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. The goal of this research is to improve the performance of machine learning models that classify fake reviews. In this work Decision tree, Random Forest, Gradient Boosting, AdaBoost, and Bi-LSTM these five machine learning approaches have been implemented to get the best performance on our dataset. Data was collected from the current Bangladesh ride-sharing applications review section. After creating & running the model, Bidirectional Long Short-Term Memory (Bi-LSTM) achieved 85% best model accuracy and 89.0 F1-macro scores with training data rather than other machine learning algorithms.