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 applies natural language processing techniques to determine whether a specific text expresses positive, negative, or neutral emotions. 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, and supervised sentiment labelling using Naïve Bayes and Random Forest. With an overall accuracy of 0.9023, Random Forest ranked above Naïve Bayes with 0.8333.