Unveiling perceptions: aspect-based sentiment analysis of Malaysia’s e-hailing reviews

Abstract

The growing demand for e-hailing services in Malaysia leads to increased competition among more than 20 licensed e-hailing service providers. Consumer satisfaction is a crucial factor influencing variables in the business organization, and understanding consumers’ perceptions is vital for service improvement. Reviews on e-hailing services are unstructured data and massive, making comparisons difficult. Thus, this study aims to classify Malaysia’s e-hailing service reviews from Google Play Store and X using latent Dirichlet allocation (LDA) and support vector machine (SVM). Aspect-based sentiment analysis (ABSA) was performed using a two-staged method, applying LDA for aspect category detection and SVM for aspect sentiment classification separately. Fare, availability, comfort, time, and convenience are five predetermined aspect categories in this study. The LDA for English and Malay achieved a perplexity of -7.31 and -7.49, respectively. Besides, the accuracy scores of SVM for English and Malay are 86.32% and 62.97%, respectively.