A transformer based approach to detect the sentiment of drivers in ride sharing platforms
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Date
2024-04Publisher
Brac UniversityAuthor
Chakraborty, SovonMetadata
Show full item recordAbstract
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.