Authors - Shane Maluleke, Tebatso Gorgina Moape, Ernest Mnkandla Abstract - The exponential growth of consumer and service provider reviews on digital platforms has generated substantial big data online, creating both opportunities and challenges for business analytics. Businesses often use these reviews to evaluate customer satisfaction, service quality, and overall brand perception. Due to the vast amount of data generated, traditional analysis methods are often inadequate for efficient processing. Hence, most companies employ sentiment analysis techniques to analyze substantial volumes of data from reviews. Sentiment analysis is a subset of natural language processing that enables the automatic classification of text-based feedback according to the emotional tone expressed. In this paper, sentiment analysis is conducted on the Uber driver app, a ride e-hailing service, within the South African context. This study intentionally focused on driver reviews instead of customer reviews, as most research predominantly focuses on passenger satisfaction, service quality, and pricing strategies, while driver perspectives remain understudied. The methodology employed in the paper involved a systematic data mining process, followed by text pre-processing, thematic code analysis of the collected data, and the application of Naïve Bayes and Random Forest algorithms to classify the Uber driver app reviews. The Random Forest model outperformed the Naïve Bayes classifier, with an accuracy of 0.9023, while Naïve Bayes achieved an accuracy of 0.8333.
