MINING PUBLIC OPINION ON RIDE-HAILING SERVICE PROVIDERS USING ASPECT-BASED SENTIMENT ANALYSIS

被引：8

作者：

Surjandari, Isti ^{[1
]}

Wayasti, Reggia Aldiana ^{[1
]}

Zulkarnain ^{[1
]}

Laoh, Enrico ^{[1
]}

Rus, Annisa Marlin Masbar ^{[1
]}

Prawiradinata, Irfan ^{[2
]}

机构：

[1] Univ Indonesia, Fac Engn, Dept Ind Engn, Kampus UI Depok, Depok 16424, Indonesia

[2] BCG, Sampoerna Strateg Sq, Jakarta 12930, Indonesia

来源：

INTERNATIONAL JOURNAL OF TECHNOLOGY | 2019年 / 10卷 / 04期

关键词：

Aspect-based sentiment analysis; Latent Dirichlet Allocation; Net Reputation Score; Ride-hailing service; Support Vector Machine; Text mining; TWITTER SENTIMENT; PRODUCTS;

D O I：

10.14716/ijtech.v10i4.2860

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

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.

引用

页码：818 / 828

页数：11

共 23 条

[1] Probabilistic Topic Models [J].

Blei, David M. .

COMMUNICATIONS OF THE ACM, 2012, 55 (04) :77-84

[2]

Chakraborty G., 2013, Text Mining and Analysis: Practical Methods, Examples, and case Studies Using SAS(R)

[3] Sentiment detection in social networks and in collaborative learning environments [J].

Colace, Francesco ;

Casaburi, Luca ;

De Santo, Massimo ;

Greco, Luca .

COMPUTERS IN HUMAN BEHAVIOR, 2015, 51 :1061-1067

[4]

Duan J., 2015, INT C AS LANG PROC S

[5]

Grün B, 2011, J STAT SOFTW, V40, P1

[6]

Han J, 2012, MOR KAUF D, P1

[7] Social media competitive analysis and text mining: A case study in the pizza industry [J].

He, Wu ;

Zha, Shenghua ;

Li, Ling .

INTERNATIONAL JOURNAL OF INFORMATION MANAGEMENT, 2013, 33 (03) :464-472

[8] A comparison of methods for multiclass support vector machines [J].

Hsu, CW ;

Lin, CJ .

IEEE TRANSACTIONS ON NEURAL NETWORKS, 2002, 13 (02) :415-425

[9]

Lim K. W., 2012, 21 ACM INT C INF KNO

[10] Ranking products through online reviews: A method based on sentiment analysis technique and intuitionistic fuzzy set theory [J].

Liu, Yang ;

Bi, Jian-Wu ;

Fan, Zhi-Ping .

INFORMATION FUSION, 2017, 36 :149-161

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