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http://dx.doi.org/10.13106/jafeb.2020.vol7.no6.457

Insights Discovery through Hidden Sentiment in Big Data: Evidence from Saudi Arabia's Financial Sector  

PARK, Young-Eun (Management Department, College of Business Administration, Prince Sultan University)
JAVED, Yasir (Computer Science Department, College of Computer & Information Sciences, Prince Sultan University)
Publication Information
The Journal of Asian Finance, Economics and Business / v.7, no.6, 2020 , pp. 457-464 More about this Journal
Abstract
This study aims to recognize customers' real sentiment and then discover the data-driven insights for strategic decision-making in the financial sector of Saudi Arabia. The data was collected from the social media (Facebook and Twitter) from start till October 2018 in financial companies (NCB, Al Rajhi, and Bupa) selected in the Kingdom of Saudi Arabia according to criteria. Then, it was analyzed using a sentiment analysis, one of data mining techniques. All three companies have similar likes and followers as they serve customers as B2B and B2C companies. In addition, for Al Rajhi no negative sentiment was detected in English posts, while it can be seen that Internet penetration of both banks are higher than BUPA, rarely mentioned in few hours. This study helps to predict the overall popularity as well as the perception or real mood of people by identifying the positive and negative feelings or emotions behind customers' social media posts or messages. This research presents meaningful insights in data-driven approaches using a specific data mining technique as a tool for corporate decision-making and forecasting. Understanding what the key issues are from customers' perspective, it becomes possible to develop a better data-based global strategies to create a sustainable competitive advantage.
Keywords
Saudi Arabia; Financial Sector; Big Data; Social Media; Sentiment Analysis;
Citations & Related Records
Times Cited By KSCI : 11  (Citation Analysis)
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1 Park, Y. E. & Alenezi, M. (2018). Predicting the Popularity of Saudi Multinational Enterprises Using a Data Mining Technique. Journal of Management Information and Decision Sciences, 21(1), 1-14. https://www.abacademies.org/articles/predictingthe-popularity-of-saudi-multinational-enterprises-using-adata-mining-technique-7791.html
2 Abdulaziz B.A. (2013). A survey of social media users in Saudi Arabia to explore the roles, motivations and expectations toward using social media for social and political purposes. Master Thesis, Arkansas State University.
3 Abramowitz, A. (1988). An improved model for predicting presidential election outcomes. PS:Political Science and Politics, 21(4), 843-847. DOI: 10.2307/420023 https://www.jstor.org/stable/420023   DOI
4 Aljazira Capital (2018). Saudi Banking / Insurance Sector (Dec, 2018). Aljazira Capital.
5 Amigibulls. (2015). How to predict stock market trends through social media, Retrieved from Amigobulls.com.
6 Azam, O. (2015). Social media impact on arab spring, a comparison study between four middle eastern countries. Master Thesis, Hawaii Pacific University.
7 Baptista, J., Wilson, A.D., Galliers, R.D. & Bynghall S. (2017). Social Media and the Emergence of Reflexiveness as a New Capability for Open Strategy. Long Range Planning. 50(2017). 322-336. Doi.org/10.1016/j.lrp.2016.07.005   DOI
8 Bassam, A. (2015). Does Saudi Arabia's economy benefit from foreign investments? Benchmarking: Bradford, 22(7), 1214-1228.   DOI
9 Becker, K., & Lee, J. W. (2019). Organizational Usage of Social Media for Corporate Reputation Management. Journal of Asian Finance, Economics and Business, 6(1), 231-240. http://doi.org/10.13106/jafeb.2019.vol6.no1.231   DOI
10 Campbell, J. E. & Garand, J. C. (Eds.). (2000). Before the vote: Forecasting American national elections. Thousand Oaks CA: Sage.
11 Chen, H., P. De, Y. J. Hu, & B. H. Hwang. (2014). Wisdom of crowds: The value of stock opinions transmitted through social media. Review of Financial Studies, 27(2014), 1367-1403. doi:10.1093/rfs/hhu001.   DOI
12 Fishbein, M., Azjen, I., & Hinkle, R. (1980). Predicting and understanding voting in American elections: Effects of external variables. In I. Azjen and M. Fishbein (Eds.), Understanding and predicting behavior, 176-195. Englewood Cliffs, NJ: Prentice Hall.
13 Choi, C. I., Choi, J. H., Kim, C. M., & Lee, D. K. (2019). The Smart City Evolution in South Korea: Findings from Big Data Analytics. Journal of Asian Finance, Economics and Business, 7(1), 301-311. https://doi.org/10.13106/jafeb.2019.vol6.no4.179   DOI
14 Dess, G. G., Lumpkin, G. T., Eisner, A. B. & McNamara G. (2014). Strategic Management: text and cases. New York, NY: McGraw-Hill Education.
15 Ellison, N. B., & Boyd, D. M. (2013). Sociality through social network sites. In The Oxford Handbook of Internet Studies, ed. W. H. Dutton, 151-72. Oxford, UK: Oxford University Press.
16 Huang, J., Baptista, J., Newell, S. (2015). Communicational ambidexterity as a new capability to manage social media communication within organizations. The Journal of Strategic Information Systems, 24(2), 49-64. http://dx.doi.org/ 10.1016/j.jsis.2015.03.002   DOI
17 Javed, Y., Khan, A., Qureshi, B., & Chaudhry, J. (2015). Estimating Diabetic cases in KSA through search trends and Creating Cyber Diabetic Community, International Conference on Recent Advances in Computer Systems, Atlantis Press
18 Jiang, Y., Muhammad, H. A. N., & Mishal, H. N. (2018). Using Social Influence Processes and Psychological Factors to Measure Pervasive Adoption of Social Networking Sites: Evidence from Pakistan, Emerging Markets Finance and Trade, 54(15), 3485-3499, DOI: 10.1080/1540496X.2017.1417834   DOI
19 Lewis-Beck, M.S., & Rice, T. W. (1984). Forecasting US.S House elections. Legislative Studies Quarterly, 9(30), 475-486. DOI: 10.2307/439492 https://www.jstor.org/stable/439492   DOI
20 Kavoossi, M. (2000). The globalization of business and the Middle East: Opportunities and constraints. Westport, CT: Quorum Books. Li
21 Soyiri I. N. & Reidpath D. D. (2013). An overview of health forecasting. Environ Health Prev Med. 18(1): 1-9. Published online 2012 Jul 28. doi: 10.1007/s12199-012-0294-6   DOI
22 Park, Y.E. (2019). Data Empowered Insights for Sustainability of Korean MNEs. Journal of Asian Finance, Economics and Business, 6(3), 173-183. https://doi.org/10.13106/jafeb.2019.vol6.no3.173   DOI
23 Razmerita, L., Kirchner, K., & Nabeth, T. (2014). Social media in organizational: leveraging personal and collective knowledge processes. Journal of Organizational Computing and Electronic Commerce, 24(1), 74-93. DOI: 10.1080/10919392.2014.866504   DOI
24 Roberto Dell'Anno, Thierry R. & Offiong H. S. (2016). Impact of social media on economic growth - evidence from social media, Applied Economics Letters, 23(9), 633-636, DOI: 10.1080/13504851.2015.1095992   DOI
25 SAMA (2019). Financial Stability report, Saudi Arabian Monetary Authority.
26 Shweta, K. (2012). Using data mining techniques for diagnosis and prognosis of cancer disease. International Journal of Computer Science, Engineering and Information Technology, 2(2), 55-66. DOI: 10.5121/ijcseit.2012.2206   DOI
27 Sutanto, J., Tan, C. H., Battistini, B., & Phang, C.W. (2011). Emergent leadership in virtual collaboration settings: a social network analysis approach. Long Range Planning, 44(5), 421-439. DOI: 10.1016/j.lrp.2011.09.001   DOI
28 Whitely, P., Sanders, D., Stewart, M., & Clarke, H. (2011). Aggregate level forecasting of the 2010 general election in Britain: The Seats-Votes model. Electoral Studies, 30(2), 278-283. DOI: 10.1016/j.electstud.2010.09.010   DOI
29 Whitely, P. F. (2005). Forecasting seats from votes in British general elections. The British Journal of Politics & International Relations, 7(2), 165-173. DOI: 10.1111/j.1467-856X.2005.00179.x   DOI
30 Majchrzak, A., Wagner, C., & Yates, D. (2013). The impact of shaping on knowledge for organizational improvement with wikis. MIS Quarterly, 37(2), 455-A412. DOI: 10.25300/MISQ/2013/37.2.07   DOI
31 Majer I. (2011). Modeling and forecasting health expectancy: theoretical framework and application. In: Netspar Discussion Papers: 01/2011-009. Network for Studies on Pensions, Aging and Retirement. 2011. http://arno.uvt.nl/show.cgi?fid=113977.
32 Nadeau, R., Lewis-Beck, M.S., & Belanger, E. (2009). Election forecasting in the United Kingdom: A two-step model. Journal of Elections, Public Opinion & Parties, 19(3), 333-358. https://doi.org/10.1080/17457280903074276   DOI
33 Nguyen, T. (2018). The impact of hallyu 4.0 and social media on korean products purchase decision of generation C in Vietnam. Journal of Asian Finance, Economics and Business, 5(3), 81-93. http://doi.org/10.13106/jafeb.2018.vol5.no3.81   DOI
34 Park, Y. E., Allui, A., & Alselaimi, R. (2017). Determinants of entry modes choice for MNEs: Exploring major challenges and implications for Saudi Arabia. 1st AUE International Research Conference, in Dubai UAE, Springer. https://doi.org/10.1007/978-3-030-01662-3
35 Park, Y. E., Chaffar, S., Kim, M.S., & Ko, H.Y., (2017). Predicting Arab consumers preferences on the Korean contents distribution. Journal of Distribution Science, 15(4), 33-40. DOI:10.15722/jds.15.4.201704.33   DOI
36 Park, Y. E. (2018). The endless challenges of KIA motors for globalization: A case study on Kia in Saudi Arabia. International Journal of Industrial Distribution & Business, 9(9), 45-52. DOI:10.13106/ijidb.2018.vol9.no9.45.   DOI