A Framework for Social Media Data Mining and Analysis to Product and Service Development- Case of The Zimbabwean National Art Gallery

Authors

  • Kumbirayi Kwenda  Systems Analyst, Kunda.org, Harare, Zimbabwe
  • Noreen Sarai  Computer Science, Midlands State University, Gweru, Zimbabwe
  • Tinashe Gwendolyn Zhou   Information Systems, Midlands State University, Gweru, Zimbabwe

DOI:

https://doi.org/10.32628/CSEIT217121

Keywords:

Social Media, Data Mining, Sentiment Analysis, Knowledge Data Discovery

Abstract

Social networks have become a vital component in personal life. People are addicted to social network features, updating their profile page and collaborating virtually with other members have become daily routines. Web data mining is a new trend in current research studies. This study sought to develop a framework for social media data mining and analysis for the betterment and advancement of products (art effects) and services (exhibitions) with in the contemporary art industry of Zimbabwe through a case study of the Zimbabwean National Art Gallery. The information for this paper was gathered through the use of in-depth interviews, questionnaires, key-word search and API from the organization’s social media twitter handle were used to get the data for analysis. Focus group participants were chosen from the National art gallery and a matching number was selected from the artist who makes the artworks which will be soles or exhibited through the national art gallery. The findings suggested that, yes it is possible to inform the next batch of products with information mined from analyzing the sentiments or reviews of the previous set of artworks. Hence with this in mind the researchers managed to develop a framework which can be used to implement social media mining in the art sector of Zimbabwe. The proposed framework tried to handle the major limitations in current web mining frameworks by handling challenges such as special symbols, slang use, Data Validity analysis, time frame, and methodologies.

References

  1. Barbier, G. (2011) 'Social Network Data Analytics', Social Network Data Analytics. doi: 10.1007/978-1-4419-8462-3.
  2. Bertoni, M. and Chirumalla, K. (2011) 'Leveraging web 2.0 in new product development: Lessons learned from a cross-company study', Journal of Universal Computer Science, 17(4), pp. 548–564. doi: 10.3217/jucs-017-04-0548.
  3. Bijmolt, T. H. A. et al. (2010) 'Analytics for customer engagement', Journal of Service Research, 13(3), pp. 341–356. doi: 10.1177/1094670510375603.
  4. Broglio, S. P. et al. (2014) 'National athletic trainers' association position statement: Management of sport concussion', Journal of Athletic Training, 49(2), pp. 245–265. doi: 10.4085/1062-6050-49.1.07.
  5. Dang-xuan, S. S. L. (2012) 'Social media and political communication : a social media analytics framework'. doi: 10.1007/s13278-012-0079-3.
  6. Dellarocas, C., Gao, G. and Narayan, R. (2010) 'Are Consumers More Likely to Contribute Online Reviews for Hit or Niche Products?', Journal of Management Information Systems. Routledge, 27(2), pp. 127–158. doi: 10.2753/MIS0742-1222270204.
  7. Fan, W. and Gordon, M. D. (2014) 'The power of social media analytics', Communications of the ACM, 57(6), pp. 74–81. doi: 10.1145/2602574.
  8. Farr, B. C. (2008) 'Designing Qualitative Research', Transformation: An International Journal of Holistic Mission Studies, 25(2–3), pp. 165–166. doi: 10.1177/026537880802500310.
  9. Fernando, G., Gapar, M. and MdJohar, M. (2015) 'Framework for Social Network Data Mining', International Journal of Computer Applications, 116(18), pp. 7–10. doi: 10.5120/20434-2765.
  10. Gundecha, P. and Liu, H. (2012) 'Mining Social Media: A Brief Introduction', 2012 TutORials in Operations Research, (Dmml), pp. 1–17. doi: 10.1287/educ.1120.0105.
  11. Gundechahuan, P. and Liu, H. (2014) 'INFORMS Tutorials in Operations Research Mining Social Media: A Brief Introduction', INFORM Tutorials in Operations Research., (November 2018), pp. 1–17. doi: 10.1287/educ.1120.0105.
  12. He, W. et al. (2015) 'A novel social media competitive analytics framework with sentiment benchmarks', Information and Management, 52(7), pp. 801–812. doi: 10.1016/j.im.2015.04.006.
  13. Hill, S. and Ready-campbell, N. (2011) 'Expert Stock Picker : The Wisdom of ( Experts in ) Crowds Expert Stock Picker : The Wisdom of ( Experts in ) Crowds', 15, pp. 73–102.
  14. Holsapple, C., Hsiao, S.-H. and Pakath, R. (2014) 'Business social media analytics: Definition, benefits, and challenges: Completed research paper', 20th Americas Conference on Information Systems, AMCIS 2014, (2010), pp. 1–12.
  15. J., K. K. (2006) 'Information security: management's effect on culture and policy', Information Management & Computer Security. Edited by M. T. E. Emerald Group Publishing Limited, 14(1), pp. 24–36. doi: 10.1108/09685220610648355.
  16. Joseph, S. R., Letsholo, K. and Hlomani, H. (2017a) 'A Conceptual Framework for the Mining and Analysis of the Social Media Data', International Journal of Database Theory and Application, 10(10), pp. 11–34. doi: 10.14257/ijdta.2017.10.10.02.
  17. Joseph, S. R., Letsholo, K. and Hlomani, H. (2017b) 'A Conceptual Framework for the Mining and Analysis of the Social Media Data', 10(10), pp. 11–34.
  18. Kelly, B. et al. (2015) 'New Media but Same Old Tricks: Food Marketing to Children in the Digital Age', Current obesity reports, 4(1), pp. 37–45. doi: 10.1007/s13679-014-0128-5.
  19. Kurniawati, Nargiza, B. and Graeme, S. (2013) 'The business impact of social media analytics', ECIS 2013 - Proceedings of the 21st European Conference on Information Systems.
  20. Manuscript, A. (2015) 'Europe PMC Funders Group Global , regional and national prevalence of overweight and obesity in children and adults 1980-2013 : A systematic analysis', 384(9945), pp. 766–781. doi: 10.1016/S0140-6736(14)60460-8.Global.
  21. Of, M., In, S. and Systems, I. (2008) 'Master of Science in Information Systems'.
  22. Panayides, P. M. and Cullinane, K. (2002) 'Competitive Advantage in Liner Shipping: A Review and Research Agenda', International journal of maritime economics, 4(3), pp. 189–209. doi: 10.1057/palgrave.ijme.9100045.
  23. Panisson, A. et al. (2012) 'On the dynamics of human proximity for data diffusion in ad-hoc networks', Ad Hoc Networks, 10(8), pp. 1532–1543. doi: 10.1016/j.adhoc.2011.06.003.
  24. Peng, Y. et al. (2006) 'A systemic framework for the field of data mining and knowledge discovery', Proceedings - IEEE International Conference on Data Mining, ICDM, 7(4), pp. 395–399.
  25. Qi, L. and Zhang, S. (2012) 'The Development of Customer Relationship Management System Based on Rough Set', Communications in Computer and Information Science, 315, pp. 328–333. doi: 10.1007/978-3-642-34240-0_43.
  26. Sahay, A. (2016) 'Peeling Saunder's Research Onion', ResearchGate, (October), pp. 1–6.
  27. Salkind, N. (2010) 'Encyclopedia of Research Design'. Thousand Oaks, California. doi: 10.4135/9781412961288.
  28. Sengweni, W. et al. (2015) 'TITLE A Lotka-Volterra competition model for modelling market competition in the telecommunication industry : Case Study of Zimbabwe .'
  29. Sharma, R. et al. (2010) 'Business analytics and competitive advantage: A review and a research agenda', Frontiers in Artificial Intelligence and Applications, 212, pp. 187–198. doi: 10.3233/978-1-60750-577-8-187.
  30. Widener, M. J. and Li, W. (2014) 'Using geolocated Twitter data to monitor the prevalence of healthy and unhealthy food references across the US', Applied Geography. Elsevier Ltd, 54, pp. 189–197. doi: 10.1016/j.apgeog.2014.07.017.
  31. Wuensch, K. L. (2011) 'Chi-Square Tests', International Encyclopedia of Statistical Science, pp. 252–253. doi: 10.1007/978-3-642-04898-2_173.
  32. Zhao, X. and Ohsawa, Y. (2018) 'Sentiment Analysis on the Online Reviews Based on Hidden Markov Model', Journal of Advances in Information Technology, 9(2), pp. 33–38. doi: 10.12720/jait.9.2.33-38.

IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2021-02-28

Issue

Volume 7, Issue 1, January-February-2021

Section

Research Articles

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How to Cite

[1]
Kumbirayi Kwenda, Noreen Sarai, Tinashe Gwendolyn Zhou , " A Framework for Social Media Data Mining and Analysis to Product and Service Development- Case of The Zimbabwean National Art Gallery" International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 7, Issue 1, pp.194-209, January-February-2021. Available at doi : https://doi.org/10.32628/CSEIT217121