Mining Public Opinion on Ride-Hailing Service Providers using Aspect-Based Sentiment Analysis

• Main Authors: Isti Surjandari, Reggia Aldiana Wayasti, Zulkarnain, Enrico Laoh, Annisa Marlin Masbar Rus, Irfan Prawiradinata
• Format: Article
• Language: English
• Published: Universitas Indonesia 2019-07-01
• Series: International Journal of Technology
• Subjects: Aspect-based sentiment analysis; Latent Dirichlet Allocation; Net Reputation Score; Ride-hailing service; Support Vector Machine; Text mining
• Online Access: http://ijtech.eng.ui.ac.id/article/view/2860
• ISSN: 2086-9614; 2087-2100

The use of ride-hailing services as a solution to current transportation problems is currently attracting much attention. Their benefits and convenience mean many people use them in their everyday lives and discuss them in the social media. As a result, ride-hailing service providers utilize social media to capture customers’ opinions and to market their services. If these opinions and comments are analyzed, service providers can obtain feedback to evaluate their services in order to achieve customer satisfaction. This study combines the text mining approach, in the form of aspect-based sentiment analysis to identify topics in customer opinions and their sentiments, with scoring of ride-hailing service providers in general, and more specifically based on the topics and sentiments. The study analyzes customers’ opinions on Twitter of three ride-hailing service providers. Text data were classified based on six topics derived from the topic modeling process, along with the sentiments expressed on them. Scoring of the three ride-hailing service providers was based on the number of positive and negative comments in relation to each topic, as well as overall comments. The results of the study can be used as input to evaluate and improve the service in Indonesia, thus the customer satisfaction and loyalty can be maintained and improved.