Enterprise Competitive Analysis and Consumer Sentiments on Social Media - Insights from Telecommunication Companies

Eric Afful-Dadzie, Stephen Nabareseh, Zuzana Komínková Oplatková, Petr Klímek

Abstract

The utilization of Social media tools in business enterprises has tremendously increased with an increased number of users and a corresponding upsurge in time spent online. Online social media services such as Facebook and Twitter are used by companies to introduce new products and services, provide various supports and interact with customers on daily basis. This regular interaction of businesses and consumers results in huge amount of customer-generated content which is becoming a source of insight in analysing the often erratic consumer behaviour. For companies to harness the business potential of social media to increase competitive advantage, sentiments behind textual data of both their customers and that of their competitors must be keenly monitored and analysed. This paper demonstrates how companies especially those in the Telecommunication industry can seize the opportunity presented by social media to mine textual data to gain advantage over competitors by cumulatively understanding consumer opinions, frustrations and satisfaction. Using Facebook and Twitter sites of the top three telecommunication companies in Ghana: MTN, Vodafone and Tigo the paper reveals insights from unstructured texts of customers of these three companies. The results show (1) the exponential growth of social media users in Ghana (2) impact and numbers behind active social media participation in the telecommunication industry (3) the power of social media opinion mining for competitive analysis (4) how business value could be extracted from the huge unstructured textual data available on social media and (5) the company that is more responsive to customer concerns.

References

  1. Aggarwal, R., Gopal, R., Sankaranarayanan, R., & Singh, P. V., 2012. Blog, blogger, and the firm: Can negative employee posts lead to positive outcomes? Information Systems Research, 23(2), 306-322.
  2. Akehurst, G., 2009. User generated content: The use of blogs for tourism organizations and tourism consumers. Service Business, 3(1), 51-61.
  3. Bagheri, A., Saraee, M., De Jong, F., 2013. Care more about customers: Unsupervised domain-independent aspect detection for sentiment analysis of customer reviews. Knowledge-Based Systems, 52, 201-213.
  4. Barker, P., 2008. How social media is transforming employee communications at Sun Microsystems. Global Business and Organizational Excellence, 27(4), 6-14.
  5. Barlow, M., Thomas, D. B., 2010. The executive's guide to enterprise social media strategy: how social networks are radically transforming your business (Vol. 42). John Wiley & Sons.
  6. Bradley, A. J., McDonald, M. P., 2011. The Social Organization: How to Use Social Media to Tap the Collective Genius of Your Customers and Employees. Harvard Business Press.
  7. Chavula, H. K. 2013. Telecommunications development and economic growth in Africa. Information Technology for Development, 19(1), 5-23.
  8. Dave, K., Lawrence, S., Pennock, D. M., 2003. Mining the peanut gallery: Opinion extraction and semantic classification of product reviews. In Proceedings of the 12th international conference on World Wide Web (pp. 519-528). ACM.
  9. Deloitte, Touche, 2012. Social Media Africa. Africa Frontiers Forum
  10. Dyer, P., 2013. Top Tools for Social Media Monitoring, Analytics, and Management. Retrieved from: http://socialmediatoday.com/pamdyer/1458746/50- top-tools-social-media-monitoring-analytics-andmanagement-2013
  11. Esuli, A., Sebastiani, F., 2006. SentiWordNet: A publicly available lexical resource for opinion mining. In Proceedings of LREC (Vol. 6, pp. 417-422).
  12. Essoungou, A.M., 2010. A social media boom begins in Africa. United Nations, Africa Renewal. Retrieved from:http://www.un.org/africarenewal/magazine/dece mber-2010/social-media-boom-begins-africa
  13. Feldman, R., 2013. Techniques and applications for sentiment analysis. Communications of the ACM, 56(4), 82-89.
  14. Ghiassi, M., Skinner, J., Zimbra, D., 2013. Twitter brand sentiment analysis: A hybrid system using n-gram analysis and dynamic artificial neural network. Expert Systems with Applications, 40(16), 6266-6282.
  15. Gillin, P., Schwartzman, E., 2011. Social marketing to the business customer: Listen to your B2B market, generate major account leads, and build client relationships, John Wiley & Sons. New Jersey, 1st edition.
  16. Governatori, G., Iannella, R., 2011. A modeling and reasoning framework for social networks policies. Enterprise Information Systems, 5(1), 145-167.
  17. Harvard Business Review, 2010. The New Conversation: Taking Social Media from Talk to Action. SAS Institute. Harvard.
  18. Jussila, J. J., Kärkkäinen, H., Aramo-Immonen, H., 2014. Social media utilization in business-to-business relationships of technology industry firms. Computers in Human Behavior, 30, 606-613.
  19. Kang, D., Park, Y., 2014. Review-based measurement of customer satisfaction in mobile service: Sentiment analysis and VIKOR approach. Expert Systems with Applications, 41(4), 1041-1050.
  20. Koppel, M., Schler, J., 2006. The importance of neutral examples for learning sentiment. Computational Intelligence, 22(2), 100-109.
  21. Lipsman, A., Mudd, G., Rich, M., Bruich, S., 2012. The power of "like": How brands reach (and influence) fans through social-media marketing. Journal of Advertising Research, 52 (1), 40-52
  22. Mahmoud, M. A., Hinson, R. E., 2012. Market orientation, innovation and corporate social responsibility practices in Ghana's telecommunication sector. Social Responsibility Journal, 8(3), 327-346.
  23. Mittal, B., Lassar, W. M., 1996. The role of personalization in service encounters. Journal of retailing, 72(1), 95-109.
  24. NCA-Ghana, 2013. Review of mobile and fixed network services statistics for the year 2013. Retrieved from: http://www.nca.org.gh/73/34/News.html?item=357
  25. Pang, B., Lee, L., 2008. Opinion mining and sentiment analysis. Foundations and trends in information retrieval, 2(1-2), 1-135.
  26. Pang, B., Lee, L., Vaithyanathan, S., 2002. Thumbs up?: sentiment classification using machine learning techniques. In Proceedings of the ACL-02 conference on Empirical methods in natural language processingVolume 10 (pp. 79-86). Association for Computational Linguistics.
  27. Peppers, D., Rogers, M., 1995. A new marketing paradigm: share of customer, not market share. Managing Service Quality, 5(3), 48-51.
  28. Qualman, E., 2009. Socialnomics: how social media transforms the way we live and do business. Wiley John & Sons, Inc. Hoboken, 2nd edition.
  29. Safko, L., 2010. The Social media bible: tactics, tools, and strategies for business success. John Wiley & Sons. Hoboken, New Jersey, 2nd edition.
  30. Sashi, C. M., 2011. Customer engagement, buyer-seller relationships and social media. Management Decision, 50(2).
  31. Sinderen, M. V., Almeida, J. P. A., 2011. Empowering enterprises through nextgeneration enterprise computing. Enterprise Information Systems, 5(1), 1-8.
  32. Taboada, M., Brooke, J., Tofiloski, M., Voll, K., Stede, M., 2011. Lexicon-based methods for sentiment analysis. Computational linguistics, 37(2), 267-307.
  33. Thelwall, M., Buckley, K., Paltoglou, G., 2012. Sentiment strength detection for the social web. Journal of the American Society for Information Science and Technology, 63(1), 163-173.
  34. Tong, R. M., 2001. An operational system for detecting and tracking opinions in on-line discussion. In Working Notes of the ACM SIGIR 2001 Workshop on Operational Text Classification, Vol. 1, pp. 6.
  35. Turney, P. D., 2002. Thumbs up or thumbs down?: semantic orientation applied to unsupervised classification of reviews. In Proceedings of the 40th annual meeting on association for computational linguistics (pp. 417-424).
  36. Weber, L., 2009. Marketing to the social web: How digital customer communities build your business, John Wiley & Sons. Hoboken, NJ, 2nd edition.
  37. Wiebe, J. M., 1994. Tracking point of view in narrative. Computational Linguistics, 20(2), 233-287.
  38. Wollan, R., Smith, N., Zhou, C., 2010. The social media management handbook: everything you need to know to get social media working in your business. John Wiley & Sons. Hoboken, 1st edition.
  39. Zeng, L., Li, L., Duan, L., 2012. Business intelligence in enterprise computing environment. Information Technology & Management, 13(4), 297-310.
  40. Zhang, W., Xu, H., Wan, W., 2012. Weakness Finder: Find product weakness from Chinese reviews by using aspects based sentiment analysis. Expert Systems with Applications, 39(11), 10283-10291.
Download


Paper Citation


in Harvard Style

Afful-Dadzie E., Nabareseh S., Komínková Oplatková Z. and Klímek P. (2014). Enterprise Competitive Analysis and Consumer Sentiments on Social Media - Insights from Telecommunication Companies . In Proceedings of 3rd International Conference on Data Management Technologies and Applications - Volume 1: DATA, ISBN 978-989-758-035-2, pages 22-32. DOI: 10.5220/0004991300220032


in Bibtex Style

@conference{data14,
author={Eric Afful-Dadzie and Stephen Nabareseh and Zuzana Komínková Oplatková and Petr Klímek},
title={Enterprise Competitive Analysis and Consumer Sentiments on Social Media - Insights from Telecommunication Companies},
booktitle={Proceedings of 3rd International Conference on Data Management Technologies and Applications - Volume 1: DATA,},
year={2014},
pages={22-32},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004991300220032},
isbn={978-989-758-035-2},
}


in EndNote Style

TY - CONF
JO - Proceedings of 3rd International Conference on Data Management Technologies and Applications - Volume 1: DATA,
TI - Enterprise Competitive Analysis and Consumer Sentiments on Social Media - Insights from Telecommunication Companies
SN - 978-989-758-035-2
AU - Afful-Dadzie E.
AU - Nabareseh S.
AU - Komínková Oplatková Z.
AU - Klímek P.
PY - 2014
SP - 22
EP - 32
DO - 10.5220/0004991300220032