A transformer based approach to detect the sentiment of drivers in ride sharing platforms

Abstract

Globally, ride-sharing is very popular, especially in developed countries. The scheme has been launched in many developing countries, and Bangladesh is no exception. The ongoing transportation problem and traffic jams make this country vulnerable economically. The impact of COVID-19 has snatched the jobs of many people. The ride-sharing platform allowed them to grab a chance to be self-dependent. On the contrary, the increasing hike in daily vehicle accessories, fuels, and parts makes it difficult for a rider to earn his bread and butter. In this research, the author focuses on the impact of ride-sharing and drivers on the Bangladeshi economy. Along with this, many social and economic statuses are analyzed. At first, a dataset was prepared after discussing it with 2234 drivers. Extensive exploratory data analysis was performed to find insightful information from the dataset. Later, the dataset is preprocessed precisely before feeding into numerous Machine Learning and Deep Learning architectures. A comment from each of the riders is also taken to understand the sentiment of these riders. Three sentiments have been considered, namely Positive, Negative, and Neutral. The researchers have adopted an optimized BERT transformer-based approach to validate the dataset and classify Bengali comments correctly. The model can outperform the state-of-the-art architectures in numerous performance metrics. The optimized model shows a 80.63% F1-score in the training dataset, whereas it shows an 84.53% F1-score in the validation set. Finally, the black box model is interpreted with the aid of Explainable Artificial Intelligence.