TY - GEN
T1 - Mining customer opinion for topic modeling purpose
T2 - 6th International Conference on Information and Communication Technology, ICoICT 2018
AU - Wayasti, Reggia Aldiana
AU - Prajitno, Isti Surjandari
AU - Zulkarnain, null
N1 - Funding Information:
The authors would like to express their gratitude to Universitas Indonesia for funding this study through Thesis Research Grants for Indexed International Publication, No 2456/UN2.R3.1/HKP.05.00/2018.
Funding Information:
ACKNOWLEDGEMENT The authors would like to express their gratitude to Universitas Indonesia for funding this study through Thesis Research Grants for Indexed International Publication, No 2456/UN2.R3.1/HKP.05.00/2018.
Publisher Copyright:
© 2018 IEEE.
PY - 2018/11/8
Y1 - 2018/11/8
N2 - The popularity of ride-hailing services in the form of smartphone application as a transportation solution has become center of attention. The convenience offered has made many people use it in daily life and discuss it on social media. As a result, ride-hailing service providers utilize social media for capturing customers' opinions and marketing their services. If customers' statements about ride-hailing services are analyzed further, service providers can get insight for evaluating their services to meet customers' satisfaction. Text mining approach can be useful to analyze large number of posts and various writing styles to extract hidden information. Furthermore, by applying topic modeling, service providers can identify the important points that were spoken by customers without previously giving label or category to the text. Latent Dirichlet Allocation was used in this study to extract topics based on the posts from ride-hailing customers published on Twitter. This study used 40 parameter combinations for LDA to get the best one to obtain the topics. Based on the perplexity value, there were 9 topics discussed by customers in their posts including the top words in each topic. The output of this study can be used for the service providers to evaluate and improve the services.
AB - The popularity of ride-hailing services in the form of smartphone application as a transportation solution has become center of attention. The convenience offered has made many people use it in daily life and discuss it on social media. As a result, ride-hailing service providers utilize social media for capturing customers' opinions and marketing their services. If customers' statements about ride-hailing services are analyzed further, service providers can get insight for evaluating their services to meet customers' satisfaction. Text mining approach can be useful to analyze large number of posts and various writing styles to extract hidden information. Furthermore, by applying topic modeling, service providers can identify the important points that were spoken by customers without previously giving label or category to the text. Latent Dirichlet Allocation was used in this study to extract topics based on the posts from ride-hailing customers published on Twitter. This study used 40 parameter combinations for LDA to get the best one to obtain the topics. Based on the perplexity value, there were 9 topics discussed by customers in their posts including the top words in each topic. The output of this study can be used for the service providers to evaluate and improve the services.
KW - Latent Dirichlet allocation
KW - Ride-hailing service
KW - Social media analytics
KW - Text mining
KW - Topic modeling
UR - http://www.scopus.com/inward/record.url?scp=85058425712&partnerID=8YFLogxK
U2 - 10.1109/ICoICT.2018.8528751
DO - 10.1109/ICoICT.2018.8528751
M3 - Conference contribution
AN - SCOPUS:85058425712
T3 - 2018 6th International Conference on Information and Communication Technology, ICoICT 2018
SP - 305
EP - 309
BT - 2018 6th International Conference on Information and Communication Technology, ICoICT 2018
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 3 May 2018 through 4 May 2018
ER -