Mining public opinion on ride-hailing service providers using aspect-based sentiment analysis

Isti Surjandari, Reggia Aldiana Wayasti, Enrico Laoh, Zulkarnain, Annisa Marlin Masbar Rus, Irfan Prawiradinata

Research output: Contribution to journalArticle

Abstract

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.

Original languageEnglish
Pages (from-to)818-828
Number of pages11
JournalInternational Journal of Technology
Volume10
Issue number4
DOIs
Publication statusPublished - Jul 2019

Keywords

  • Aspect-based sentiment analysis
  • Latent Dirichlet Allocation
  • Net Reputation Score
  • Ride-hailing service
  • Support Vector Machine
  • Text mining

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