TY - GEN
T1 - Sentiment Analysis for Mining Customer Opinion on Twitter
T2 - 5th International Conference on Information Science and Control Engineering, ICISCE 2018
AU - Zulkarnain, Zulkarnian
AU - Surjandari, Isti
AU - Wayasti, Reggia Aldiana
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/7/2
Y1 - 2018/7/2
N2 - The function of social media is now transforming to become source of information, even to support electronic word of mouth (e-WOM). Many companies, including ride-hailing service providers, can capture customers' opinions for the purpose of evaluating their products and services. Text mining can be useful to analyze great number of comments from ride-hailing customers in social media. Furthermore, by applying sentiment analysis, service providers can define the service categories which are good and still needing improvement. Customers' comments were taken from Twitter, and text classification method was used to classify the comments based on six predefined categories and their respective polarity. The accuracy of the classification model was 86% which was good to classify the text data. The output of this research is expected to give insight for ride-hailing service provider to understand customers' perspective about the services so that it will be easier to evaluate and improve their services based on the categories in this study.
AB - The function of social media is now transforming to become source of information, even to support electronic word of mouth (e-WOM). Many companies, including ride-hailing service providers, can capture customers' opinions for the purpose of evaluating their products and services. Text mining can be useful to analyze great number of comments from ride-hailing customers in social media. Furthermore, by applying sentiment analysis, service providers can define the service categories which are good and still needing improvement. Customers' comments were taken from Twitter, and text classification method was used to classify the comments based on six predefined categories and their respective polarity. The accuracy of the classification model was 86% which was good to classify the text data. The output of this research is expected to give insight for ride-hailing service provider to understand customers' perspective about the services so that it will be easier to evaluate and improve their services based on the categories in this study.
KW - multi-class classification
KW - ride-hailing service
KW - sentiment analysis
KW - social media analytics
KW - text mining
UR - http://www.scopus.com/inward/record.url?scp=85062066626&partnerID=8YFLogxK
U2 - 10.1109/ICISCE.2018.00113
DO - 10.1109/ICISCE.2018.00113
M3 - Conference contribution
AN - SCOPUS:85062066626
T3 - Proceedings - 2018 5th International Conference on Information Science and Control Engineering, ICISCE 2018
SP - 512
EP - 516
BT - Proceedings - 2018 5th International Conference on Information Science and Control Engineering, ICISCE 2018
A2 - Li, Shaozi
A2 - Dai, Ying
A2 - Cheng, Yun
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 20 July 2018 through 22 July 2018
ER -