Text Analytics of Customers on Twitter: Brand Sentiments in Customer Support

Document Type: Research Paper

Author

Assistant Professor, Department of Industrial Management, Faculty of Management and Accounting, Allameh Tabataba’i University, Tehran, Iran.

Abstract

Brand community interactions and online customer support have become major platforms of brand sentiment strengthening and loyalty creation. Rapid brand responses to each customer request though inbound tweets in twitter and taking proper actions to cover the needs of customers are the key elements of positive brand sentiment creation and product or service initiative management in the realm of intense competition. In this research, there has been an attempt to collect near three million tweets of inbound customer requests and outbound brand responses of international enterprises for the purpose of brand sentiment analysis. The steps of CRISP-DM have been chosen as the reference guide for business and data understanding, data preparation, text mining, validation of results as well as the final discussion and contribution. A rich phase of text pre-processing has been conducted and various algorithms of sentiment analysis were applied for the purpose of achieving the most significant analytical conclusions over the sentiment trends. The findings have shown that the sentiment of customers toward a brand is significantly correlated with the proper response of brands to the brand community over social media as well as providing the customers with a deep feeling of reciprocal understanding of their needs in a mid-to-long range planning.

Keywords

Main Subjects


Afful-Dadzie, E., & Afful-Dadzie, A. (2017). Liberation of public data: Exploring central themes in open government data and freedom of information research. International Journal of Information Management, 37(6), 664–672.

Agarwal, B., Mittal, N., Bansal, P., & Garg, S. (2015). Sentiment analysis using common-sense and context information. Computational Intelligence and Neuroscience, Article ID 715730, https://www.hindawi.com/journals/cin/2015/715730/

AlAlwan, A., Rana, N. P., Dwivedi, Y. K., & Algharabat, R. (2017). Social media in marketing: A review and analysis of the existing literature. Telematics and Informatics, 34(7), 1177–1190.

Antonacci, G., Fronzetti Colladon, A., Stefanini, A., & Gloor, P. (2017). It is rotating leaders who build the swarm: Social network determinants of growth for healthcare virtual communities of practice. Journal of Knowledge Management, 21(5), 1218–1239.

Arjun, M., Vivek, V., Bing, L., Natalie, G. (2013). What yelp fake review filter might be doing. In: Proceedings of The International AAAI Conference on Weblogs and Social Media (ICWSM-2013), Boston, USA.

Aswani, R., Kar, A. K., Ilavarasan, P. V., & Dwivedi, Y. K. (2018). Search engine marketing is not all gold: Insights from twitter and SEOClerks. International Journal of Information Management, 38(1), 107–116.

Bagozzi, R. P., & Dholakia, U. M. (2006). Antecedents and purchase consequences of customer participation in small group brand communities. International Journal of Research in Marketing, 23(1), 45–61.

Balbi, S., Misuraca, M., & Scepi, G. (2018). Combining different evaluation systems on social media for measuring user satisfaction. Information Processing & Management, 54(4), 674–685.

Carlson, B. D., Suter, T. A., & Brown, T. J. (2008). Social versus psychological brand community: The role of psychology sense of brand community. Journal of Business Research, 61(4), 284–291.

Chandler, J. D., Salvador, R., & Kim, Y. (2018). Language, brand and speech acts on Twitter. Journal of Product and Brand Management, 27(4), 375–384.

Chapman, P., Clinton, J., Kerber, R., Khabaza, T., Reinartz, T., Shearer, C., & Wirth, R. (2000), CRISPDM1.0: Step-by-step data mining guide¸ CRISP-DM consortium.

Csikszentmihalyi, M. (1997). Creativity: Flow and the psychology of discovery and invention perennial. Adult Education, 48(2), 121–142.

Derks, D., Fischer, A., & Bos, A. (2008). The role of emotion in computer-mediated communication: A review. Computers in Human Behavior, 24, 766–785.

Dholakia, U. M., Bagozzi, R. P., & Pearo, L. K. (2004). A social influence model of consumer participation in network and small-group-based virtual communities. International Journal of Research in Marketing, 21(3), 241–263.

Dwivedi, Y. K., Kapoor, K. K., & Chen, H. (2015). Social media marketing and advertising. The Marketing Review, 15, 289–309.

Fan, Z. P., Che, Y. J., & Chen, Z. Y. (2017). Product sales forecasting using online reviews and historical sales data: A method combining the Bass model and sentiment analysis. Journal of Business Research, 74, 90–100.

Fronzetti Colladon, A. (2018). The semantic brand score. Journal of Business Research, 88, 150–160.

Fournier, S., & Lee, L. (2009). Getting brand communities right. Harvard Business Review, 105–111 (April).

Gloor, P. A. (2017). Sociometrics and human relationships: Analyzing social networks to manage brands, predict trends, and improve organizational performance. London, UK: Emerald Publishing Limited.

Gloor, P., Fronzetti Colladon, A., Giacomelli, G., Saran, T., & Grippa, F. (2017). The impact of virtual mirroring on customer satisfaction. Journal of Business Research, 75, 67–76.

Greco, F., & Polli, A., (2019). Emotional Text Mining: Customer profiling in brand management, International Journal of Information Management. Retrieved May 10, 2019, from https://doi.org/10.1016/j.ijinfomgt.2019.04.007

Greco, F., Maschietti, D., & Polli, A. (2017). Emotional Text Mining of social networks: The French pre-electoral sentiment on migration. Rivista Italiana di Economia Demografia e Statistica, 71(2), 125–136.

Grover, P., Kar, A. K., Dwivedi, Y. K., & Janssen, M. (2018). Polarization and acculturation in US Election 2016 outcomes–can twitter analytics predict changes in voting preferences. Technological Forecasting and Social Change. https://doi.org/10.1016/j.techfore.2018.09.009

He, W., Zha, S., & Li, L. (2013). Social media competitive analysis and text mining: A case study in the pizza industry. International Journal of Information Management, 33(3), 464–472.

Jang, H., Olfma, L., Ko, I., Koh, J., & Kim, K. (2008). The influence of on-line brand community characteristics on community commitment and brand loyalty. International Journal of Electronic Commerce, 12(3), 57–80.

Jeong, B., Yoon, J., & Lee, J. M. (2017). Social media mining for product planning: A product opportunity mining approach based on topic modeling and sentiment analysis. International Journal of Information Management, 48, 280-290.

Kapoor, K. K., Tamilmani, K., Rana, N. P., Patil, P., Dwivedi, Y. K., & Nerur, S. (2018). Advances in social media research: Past, present and future. Information Systems Frontiers, 20(3), 531–558.

Koh, H.C., & Tan, G. (2011). Data mining applications in healthcare. Journal of Healthcare Information Management, 19(2), 19(2), 64-72.

Laroche, M., Habibi, M. R., Richard, M., & Sankaranarayanan, R. (2012). The effects of social media based brand communities on brand community markers, value creation practices, brand trust and brand loyalty. Computers in Human Behavior, 28(5), 1755–1767.

Leong, C., Lee, Y., & Mak, W. (2012). Mining sentiments in SMS texts for teaching evaluation. Expert Systems with Applications, 39, 2584–2589.

Lin, X., Li, Y., & Wang, X. (2017). Social commerce research: Definition, research themes and the trends. International Journal of Information Management, 37, 190–201.

Lin, C.W., Wang, K.Y., Chang, S.H., and Lin, J.A., (2019). Investigating the development of brand loyalty in brand communities from a positive psychology perspective. Journal of Business Research, 99, 446-455.

Ling, P., Geng, C., Menghou, Z., & Chunya, L. (2014). What do seller manipulations of online product reviews mean to consumers? (HKIBS Working Paper Series 070-1314) Hong Kong Institute of Business Studies, Lingnan University, Hong Kong.

Liu, B. (2012). Sentiment analysis: Mining opinions, sentiments, and emotions. Sentiment analysis: Mining opinions, sentiments, and emotions. Cambridge : Cambridge University Press.

Matthew, J.K., Spencer, G., Andrea, Z., 2015. Potential applications of sentiment analysis in educational research and practice – Is SITE the friendliest conference? In: Slykhuis, D., Marks, G. (Eds.), Proceedings of Society for Information Technology & Teacher Education International Conference 2015. Association for the Advancement of Computing in Education (AACE), Chesapeake, VA.

Mohamed Hussein, D.M.E.D, (2018), A survey on sentiment analysis challenges, Journal of King Saud University – Engineering Sciences, 30, 330-338

Mostafa, M.M., (2013), More than words: Social networks’ text mining for consumer brand sentiments, Expert Systems with Applications, 40, 4241–4251

Raeesi Vanani, I. (2017). Designing a predictive analytics for the formulation of intelligent decision making policies for VIP customers investing in the bank. Journal of Information Technology Management, 9(3), 477-511.

Raeesi Vanani, I., & Jalali, S.M.J. (2017). Analytical evaluation of emerging scientific trends in business intelligence through the utilization of burst detection algorithm. International Journal of Bibliometrics in Business and Management, 1(1), 70-79.

Raeesi Vanani, I., & Jalali, S.M.J. (2018). A comparative analysis of emerging scientific themes in business analytics. International Journal of Business Information Systems, 29(2), 183-206.

Rekik, R., Kallel, I., Casillas, J., & Alimi, A. M. (2018). Assessing web sites quality: A systematic literature review by text and association rules mining. International Journal of Information Management, 38, 201–216.

Singh, J. P., Dwivedi, Y. K., Rana, N. P., Kumar, A., & Kapoor, K. K. (2017). Event classification and location prediction from tweets during disasters. Annals of Operations Research, 1–21.

Scott, A. T., & Rajiv, K. S. (2008). Brand communities and new product adoption: The influence and limits of oppositional loyalty. Journal of Marketing, 72(6), 65–80.

Shirdastian, H., Laroche, M., & Richard, M. O. (2017). Using big data analytics to study brand authenticity sentiments: The case of Starbucks on Twitter. International Journal of Information Management, 48, 291-307.

Sohrabi, B., Raeesi Vanani, I., & Abedin, B. (2018). Human resources management and information systems trend analysis using text clustering. International Journal of Human Capital and Information Technology Professionals, 9(3), 1-24.

Sohrabi, B., Raeesi Vanani, I., & Baranizade Shineh, M. (2017). Designing a predictive analytics solution for evaluating the scientific trends in information systems domain. Webology, 14(1), 32-52.

Sohrabi, B., Raeesi Vanani, I., & Namavar, M. (2019). investigation of trends and analysis of hidden new patterns in prominent news agencies of Iran using data mining and text mining algorithms, Webology, 16(1), 114-137.

Tawunrat, C., & Jeremy, E. (2015). Chapter information science and applications, simple approaches of sentiment analysis via ensemble learning. Lecture Notes in Electrical Engineering, 339.

Weber, L. (2009). Marketing to the social web: How digital customer communities build your business. London: Wiley.

Xu, X., Wang, X., Li, Y., & Haghighi, M. (2017). Business intelligence in online customer textual reviews: Understanding consumer perceptions and influential factors. International Journal of Information Management, 37, 673–683.

Zagal, J., Tomuro, N., & Shepitsen, A. (2012). Natural language processing in game studies research: An overview. Simulation and Gaming, 43, 356–373.

Zaglia, M. E. (2013). Brand communities embedded in social networks. Journal of Business Research, 66(2), 216–223.

Zhang, C., Zeng, D., Li, J., Wang, F., & Zuo, W. (2009). Sentiment analysis of Chinese documents: From sentence to document level. Journal of the American Society for Information Science and Technology, 60, 2474–2487.