TY - JOUR
T1 - Mining public opinion on ride-hailing service providers using aspect-based sentiment analysis
AU - Surjandari, Isti
AU - Wayasti, Reggia Aldiana
AU - Laoh, Enrico
AU - Zulkarnain,
AU - Rus, Annisa Marlin Masbar
AU - Prawiradinata, Irfan
N1 - Publisher Copyright:
© IJTech 2019.
PY - 2019/7
Y1 - 2019/7
N2 - 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.
AB - 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.
KW - Aspect-based sentiment analysis
KW - Latent Dirichlet Allocation
KW - Net Reputation Score
KW - Ride-hailing service
KW - Support Vector Machine
KW - Text mining
UR - http://www.scopus.com/inward/record.url?scp=85070006812&partnerID=8YFLogxK
U2 - 10.14716/ijtech.v10i4.2860
DO - 10.14716/ijtech.v10i4.2860
M3 - Article
AN - SCOPUS:85070006812
SN - 2086-9614
VL - 10
SP - 818
EP - 828
JO - International Journal of Technology
JF - International Journal of Technology
IS - 4
ER -