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Twitter Analysis for Business Intelligence

  • Tariq Soussan
  • Marcello TrovatiEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1035)

Abstract

The evolvement of social media has made it an important and valuable part of people’s daily life all over the world. Many businesses use social media in different ways that benefit their business, such as to advertise their products and services, and as a way of strengthening relationships. Institutes utilize social media to promote programs and courses to current and prospective students, to advertise important events such as career fairs and to interact with various institute members to provide them with up to date news and information regarding their wellbeing, health, security, comfort and satisfaction. Throughout this work, web mining and opinion mining will be applied to a general business Twitter account to explore the developing themes of discussions amongst businesses’ online communities. The account will be analysed through text mining, social network analysis and sentiment analysis. It is important to monitor what topics and words are trending in high volume on specific online twitter communities as the objective is to try to conclude the sentiments of the posts posted on the Twitter account.

References

  1. 1.
    Barnes, N.G.: The Fortune 500 and social media: a longitudinal study of blogging, Twitter and Facebook usage by America’s largest companies (2010). Retrieved from Society for New Communications Research on 6 March 2011Google Scholar
  2. 2.
    Bifet, A., Frank, E.: Sentiment knowledge discovery in Twitter streaming data. In: Pfahringer, B., Holmes, G., Hoffmann, A. (eds.) DS 2010. LNCS (LNAI), vol. 6332, pp. 1–15. Springer, Heidelberg (2010).  https://doi.org/10.1007/978-3-642-16184-1_1CrossRefGoogle Scholar
  3. 3.
    Bottles, K., Sherlock, T.: Who should manage your social media strategy. Physician Executive 37(2), 68–72 (2011)Google Scholar
  4. 4.
    Campbell, D.: The new ecology of information: how the social media revolution challenges the university. Environ. Plann. D: Soc. Space 28(2), 193–201 (2010)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Daniel, B.K. (ed.): Handbook of Research on Methods and Techniques for Studying Virtual Communities: Paradigms and Phenomena: Paradigms and Phenomena. IGI Global, Hershey (2010)Google Scholar
  6. 6.
    Elgamal, M.: Sentiment analysis methodology of Twitter data with an application on Hajj season. Int. J. Eng. Res. Sci. (IJOER) 2, 82–87 (2016)Google Scholar
  7. 7.
    Grosseck, G., Holotescu, C.: Can we use Twitter for educational activities. In: 4th International Scientific Conference, eLearning and Software for Education, Bucharest, Romania, April 2008Google Scholar
  8. 8.
    Gruzd, A.: Netlytic: Software for Automated Text and Social Network Analysis (2016). http://Netlytic.org
  9. 9.
    Holotescu, C., Grosseck, G.: An empirical analysis of the educational effects of social media in universities and colleges. Internet Learn. 2(1), 5 (2013)Google Scholar
  10. 10.
    Huberman, B.A., Romero, D.M., Wu, F.: Social networks that matter: Twitter under the microscope. arXiv preprint arXiv:0812.1045 (2008)
  11. 11.
    Hughes, A.: Higher education in a Web 2.0 world. JISC report (2009)Google Scholar
  12. 12.
    Johnston, R.: Social media strategy: follow the 6 P’s for successful outreach. Alaska Bus. Mon. 27(12), 83–85 (2011)Google Scholar
  13. 13.
    Junco, R., Heiberger, G., Loken, E.: The effect of Twitter on college student engagement and grades. J. Comput. Assist. Learn. 27(2), 119–132 (2011)CrossRefGoogle Scholar
  14. 14.
    Kietzmann, J.H., Hermkens, K., McCarthy, I.P., Silvestre, B.S.: Social media? Get serious! Understanding the functional building blocks of social media. Bus. Horiz. 54(3), 241–251 (2011)CrossRefGoogle Scholar
  15. 15.
    Lovejoy, K., Waters, R.D., Saxton, G.D.: Engaging stakeholders through Twitter: how nonprofit organizations are getting more out of 140 characters or less. Public Relat. Rev. 38(2), 313–318 (2012)CrossRefGoogle Scholar
  16. 16.
    Malita, L.: Social media time management tools and tips. Procedia Comput. Sci. 3, 747–753 (2011)CrossRefGoogle Scholar
  17. 17.
    Markos-Kujbus, É., Gáti, M.: Social media’s new role in marketing communication and its opportunities in online strategy building. BCE Marketing, Marketingkommunikáció és Telekommunikáció Tanszék, Budapest (2012)Google Scholar
  18. 18.
    Mukherjee, S., Bhattacharyya, P.: Sentiment analysis: a literature survey. arXiv preprint arXiv:1304.4520 (2013)
  19. 19.
    Piskorski, M.J.: Social strategies that work. Harvard Bus. Rev. 89(11), 116–122 (2011)Google Scholar
  20. 20.
    Sultana, M., Paul, P.P., Gavrilova, M.: Identifying users from online interactions in Twitter. In: Gavrilova, M.L., Tan, C.J.K., Iglesias, A., Shinya, M., Galvez, A., Sourin, A. (eds.) Transactions on Computational Science XXVI. LNCS, vol. 9550, pp. 111–124. Springer, Heidelberg (2016).  https://doi.org/10.1007/978-3-662-49247-5_7CrossRefGoogle Scholar
  21. 21.
    Tan, A.H.: Text mining: the state of the art and the challenges. In: Proceedings of the PAKDD 1999 Workshop on Knowledge Disocovery from Advanced Databases, vol. 8, pp. 65–70, April 1999Google Scholar
  22. 22.
    Uzelac, E.: Mastering social media. Research 34(8), 44–47 (2011)Google Scholar
  23. 23.
    Zhong, N., Li, Y., Wu, S.T.: Effective pattern discovery for text mining. IEEE Trans. Knowl. Data Eng. 24(1), 30–44 (2010)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.School of ComputingEdge Hill UniversityOrmskirkUK

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