Business intelligence for social media interaction in the travel industry in Indonesia

Michael Yulianto, Abba Suganda Girsang, Reinert Yosua Rumagit

Abstract


Electronic ticket (eticket) provider services are growing fast in Indonesia, making
the competition between companies increasingly intense. Moreover, most of them have the same
service or feature for serving their customers. To get back the feedback of their customers, many
companies use social media (Facebook and Twitter) for marketing activity or communicating
directly with their customers. The development of current technology allows the company to
take data from social media. Thus, many companies take social media data for analyses. This
study proposed developing a data warehouse to analyze data in social media such as likes,
comments, and sentiment. Since the sentiment is not provided directly from social media data,
this study uses lexicon based classification to categorize the sentiment of users’ comments. This
data warehouse provides business intelligence to see the performance of the company based on
their social media data. The data warehouse is built using three travel companies in Indonesia.
As a result, this data warehouse provides the comparison of the performance based on the social
media data.


Keywords


Business intelligence, lexicon based classification, sentiment analysis, social media 1.

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References


Adriani, M., Asian, J., Nazief, B., Tahaghoghi, S.

M., & Williams, H. E. (2007). Stemming

Indonesian: A confix-stripping approach.

Journal ACM Transactions on Asian

Language Information Processing (TALIP).

Alalwan, A. A., Rana, N. P., Dwivedi, Y. K., &

Algharabatc, R. (2017). Social media in

marketing: A review and analysis of the

existing literature. Telematics and

Informatics, 1177-1190.

Atmadjati, A. (2012). Era Maskapai Saat Ini.

Yogyakarta: Leutika Prio.

Barlow, J., & Maul, D. (2000). Emotional Value:

Creating Strong Bonds with Your Customers.

San Francisco: Berrett-Koehler Pub-lishers,

Inc.

Bo, P., Lee, L., & Vaithyanathan, S. (2002).

Thumbs up? Sentiment Classification Using

Machine Learning Techniques. EMNLP.

Budiwati, S. D., & Setiawan, N. N. (2018).

Experiment on building Sundanese lexical

database based on WordNet. Journal of

Physics: Conference Series.

Chopra, F. K., & Bhatia, R. (2016). Sentiment

Analyzing by Dictionary based Approach.

International Journal of Computer

Applications, 32-34.

Deng, S., Sinha, A. P., & Zhao, H. (2017).

Adapting sentiment lexicons to domainspecific

social media texts. Decision Support

Systems, 65-76.

Girsang, A. S., & Prakoso, C. W. (2017). Data

Warehouse Development for Customer WIFI

Access Service at a Telecommunication

Company. International Journal on

Communications Antenna and Propagation.

He, W., Zha, S., & Li, L. (2013). Social media

competitive analysis and text mining: A Case

study in the pizza Industry. Internasional

Journal of Information Management, 462-472.

Moro, S., Rita, P., & Vala, B. (2016). Predicting

social media performance metrics and

evaluation of the impact on brand building: A

data mining approach. Journal of Business

Research, 3341-3351.

Ray, P., & Chakrabarti, A. (2017). Twitter

sentiment analysis for product review using

lexicon method. International Conference on

Data Management, Analytics and Innovation

(ICDMAI), 211-216.

Saragih, M. H., & Girsang, A. S. (2017).

Sentiment analysis of customer engagement

on social media in transport online.

Sustainable Information Engineering and

Technology (SIET), 24-29.

Vercellis, C. (2009). Business Intelligence: Data

Mining and Optimization for Decision

Making. Politecnico di Milano: Wiley.

Williams, S., & Williams, N. (2006). The Profit

Impact of Business Intelligence. San

Francisco: Morgan Kaufmann.


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