Applying advanced sentiment analysis for strategic marketing insights: A case study of BBVA using machine learning techniques

  1. Luis Miguel Garay Gallastegui
  2. Ricardo Reier Forradellas
  3. Sergio Luis Náñez Alonso
Journal:
Innovative Marketing

ISSN: 1816-6326

Year of publication: 2024

Volume: 20

Issue: 2

Pages: 100-115

Type: Article

DOI: 10.21511/IM.20(2).2024.09 SCOPUS: 2-s2.0-85192447236 GOOGLE SCHOLAR lock_openOpen access editor

More publications in: Innovative Marketing

Abstract

In the digital era, understanding public sentiment toward brands on social media is essential for crafting effective marketing strategies. This study applies sentiment analysis on Banco Bilbao Vizcaya Argentaria (BBVA) tweets using advanced machine learning techniques, particularly the eXtreme Gradient Boosting (XGBoost) algorithm, which showed remarkable precision (91.2%) in sentiment classification. This process involved a systematic approach to data collection, cleaning, and preprocessing. The precision of XGBoost highlights its effectiveness in analyzing social media conversations about banking. Additionally, this paper achieved improvements in neutral tweet classification, with accuracy rates at 87-88% and a reduced misclassification rate, enhancing the analysis reliability. The findings not only uncover general sentiments toward BBVA but also provide insight into how these sentiments shift in response to marketing activities and global events. This gives marketers a valuable tool for real-time assessment of campaign effectiveness and brand perception. Ultimately, employing the XGBoost algorithm for sentiment analysis offers BBVA a strategic advantage in understanding and engaging its online audience, demonstrating the significant benefits of using sophisticated machine learning in banking. The study emphasizes the crucial role of datadriven sentiment analysis in developing informed business strategies and improving customer relationships in the banking industry's competitive landscape.

Bibliographic References

  • Aarshay, J. (2016). Complete guide to parameter tuning in XGBoost with codes in Python. GitHub. Retrieved April 3, 2020, from https://github.com/analyticsvidhya/ Complete-Guide-to-Parameter- Tuning-in-XGBoost-withcodes- in-Python/blob/master/ Complete%20Guide%20to%20 Parameter%20Tuning%20in%20 XGBoost%20with%20codes%20 in%20Python.ipynb
  • American Bankers Association. (2023). Banks report active social media engagement. Retrieved from https://www.aba.com/newsresearch/ analysis-guides/socialmedia- in-banking-2023-report
  • Amolik, A., Jivane, N., Bhandari, M., & Venkatesan, M. (2016). Twitter sentiment analysis of movie reviews using machine learning techniques. International Journal of Engineering and Technology, 7(6), 1-7. Retrieved from https://www.researchgate.net/ publication/291837156_Twitter_ Sentiment_Analysis_of_Movie_ Reviews_using_Machine_Learning_ Techniques
  • Anshari, M., Almunawar, M. N., Lim, S. A., & Al-Mudimigh, A. (2019). Customer relationship management and big data enabled: Personalization & customization of services. Applied Computing and Informatics, 15(2), 94-101.
  • Auter, Z., & Fine, J. (2018). Social media campaigning: Mobilization and fundraising on Facebook. Social Science Quarterly, 99(1), 185-200. https://doi.org/10.1111/ssqu.12391
  • Balahur, A., Kozareva, Z., & Montoyo, A. (2009). Determining the polarity and source of opinions expressed in political debates. In A. Gelbukh (Ed.), Computational linguistics and intelligent text processing (pp. 468-480). Berlin, Heidelberg: Springer. https://doi. org/10.1007/978-3-642-00382- 0_38
  • Başarslan, M. S., & Kayaalp, F. (2020). Sentiment analysis with machine learning methods on social media. Advances in Distributed Computing and Artificial Intelligence Journal, 9(3), 5-15. https://doi.org/10.14201/ADCAIJ202093515
  • Bonnet, D., & Westerman, G. (2021). The new elements of digital transformation. MIT Sloan Management Review, 62, 82-89. Retrieved from https://sloanreview. mit.edu/article/the-new-elements- of-digital-transformation/
  • Borgatti, S. P., Mehra, A., Brass, D. J., & Labianca, G. (2009). Network analysis in the social sciences. Science, 323(5916), 892-895. https:// doi.org/10.1126/science.1165821
  • Boyd, D. M., & Ellison, N. B. (2007). Social network sites: Definition, history, and scholarship. Journal of Computer-Mediated Communication, 13(1), 210-230. https://doi.org/10.1111/j.1083- 6101.2007.00393.x
  • Brad, S. (2019). Demoji. Project description. Retrieved March 15, 2022, from https://pypi.org/project/ demoji/
  • Brandes, U., Freeman, L. C., & Wagner, D. (2013). Social networks. In R. Tamassia (Ed.), Handbook of graph drawing and visualization (pp. 805-839). London: Chapman & Hall. Retrieved from https://d-nb.info/1112610073/34
  • Brodka, P., Skibicki, K., Kazienko, P., & Musiał, K. (2011). A degree centrality in multi-layered social network. 2011 International conference on computational aspects of social networks (CASoN) (pp. 237-242). IEEE. Retrieved from https://ieeexplore.ieee.org/document/ 6085951
  • Chang, V. (2018). A proposed social network analysis platform for big data analytics. Technological Forecasting and Social Change, 130, 57-68. https://doi.org/10.1016/j. techfore.2017.11.002
  • Che, X., & Ip, B. (2017). Social networks in China. Chandos Publishing.
  • Dickerson, J. P., Kagan, V., & Subrahmanian, V. S. (2014). Using sentiment to detect bots on Twitter: Are humans more opinionated than bots? 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014) (pp. 620-627). IEEE. Retrieved from https://ieeexplore.ieee.org/document/ 6921650
  • Garcia-Galera, M. C., Del-Hoyo- Hurtado, M., & Fernandez-Munoz, C. (2014). Engaged youth in the Internet: The role of social networks in social active participation. Comunicar: Revista Cientifica Iberoamericana de Comunicacion y Educacion, 43(2), 35-43. Retrieved from http://eprints.rclis. org/23377/1/c4303en.pdf
  • Gartner. (2023). The state of marketing budget and strategy 2022. Insights from Gartner's annual CMO spend survey. Retrieved from https://www.gartner.co.uk/ en/marketing/research/annualcmo- spend-survey-research
  • Gill, S. S., Tuli, S., Xu, M., Singh, I., Singh, K. V., Lindsay, D., Tuli, S., Smirnova, D., Singh, M., Jain, U., Pervaiz, H., Sehgal, B., Kaila, S. S., Misra, S., Aslanpour, M. S., Mehta, H., Stankovski, V., & Garraghan, P. (2019). Transformative effects of IoT, Blockchain and Artificial Intelligence on cloud computing: Evolution, vision, trends and open challenges. Internet of Things, 8, Article 100118. https://doi. org/10.1016/j.iot.2019.100118
  • Granovetter, M. S. (1973). The strength of weak ties. American Journal of Sociology, 78(6), 1360- 1380. Retrieved from https://snap. stanford.edu/class/cs224w-readings/ granovetter73weakties.pdf
  • Gutierrez-Batista, K., Campana, J., Martinez-Folgoso, S., Vila, M., & Martin-Bautista, M. (2016). About the effects of sentiments on topic detection in social networks. International Journal of Design & Nature and Ecodynamics, 11(3), 387-395. https://doi.org/10.2495/ dne-v11-n3-387-395
  • Hariri, R. H., Fredericks, E. M., & Bowers, K. M. (2019). Uncertainty in big data analytics: Survey, opportunities, and challenges. Journal of Big Data, 6(1), Article 44. https://doi.org/10.1186/s40537- 019-0206-3
  • Hootsuite Inc. (2022). Social media trends 2022. Retrieved March 23, 2022, from https://blog. hootsuite.com/es/informe-digitalestadisticas- de-redes-sociales/
  • Huq, M. R., Ali, A., & Rahman, A. (2017). Sentiment analysis on Twitter data using KNN and SVM. (IJACSA) International Journal of Advanced Computer Science and Applications, 8(6), 19-25. Retrieved from https://thesai.org/ Publications/ViewPaper?Volume =8&Issue=6&Code=IJACSA&Ser ialNo=3
  • IAB Spain. (2021). Estudio de redes sociales 2021 [Social media study 2021]. (In Spanish). Retrieved from https://iabspain.es/estudio/ estudio-de-redes-sociales-2021/
  • Isaak, J., & Hanna, M. J. (2018). User data privacy: Facebook, Cambridge Analytica, and privacy protection. Computer, 51(8), 56-59. https://doi.org/10.1109/ MC.2018.3191268
  • Kim, D., & Ellison, N. (2022). From observation on social media to offline political participation: The social media affordances approach. New Media & Society, 24(12), 2614-2634. https://doi. org/10.1177/1461444821998346
  • Kim, S. M., & Hovy, E. (2006). Automatic identification of pro and con reasons in online reviews. Proceedings of the COLING/ACL Main Conference Poster Sessions (pp. 483-490). http://dx.doi. org/10.3115/1273073.1273136
  • Ku, L.W., Lee, L.Y., Wu, T.H., & Chen, H. H. (2005). Major topic detection and its application to opinion summarization. Proceedings of the ACM Special Interest Group on Information Retrieval (SIGIR) (pp. 627-628). Salvador, Brazil. http://dx.doi. org/10.1145/1076034.1076161
  • Kumar, A., Sangwan, S. R., & Nayyar, A. (2020). Multimedia social big data: Mining. In Multimedia big data computing for IoT applications (pp. 289-321). Singapore: Springer. http://dx.doi.org/10.1007/978-981- 13-8759-3_11
  • Lee, Y., & Kim, K. H. (2021). Enhancing employee advocacy on social media: The value of internal relationship management approach. Corporate Communications: An International Journal, 26(2), 311-327. https://doi.org/10.1108/CCIJ-05- 2020-0088
  • Micu, A., Micu, A. E., Geru, M., & Lixandroiu, R. C. (2017). Analyzing user sentiment in social media: Implications for online marketing strategy. Psychology & Marketing, 34(12), 1094-1100. https://doi. org/10.1002/mar.21049
  • Milgram, S. (1967). The small-world problem. Psychology Today, 1(1), 61-67. Retrieved from http://snap. stanford.edu/class/cs224w-readings/ milgram67smallworld.pdf
  • Molu, F., Findik, N., & Dalci, M. (2014). Enhancing user experience in financial services. International Journal of E-Services and Mobile Applications, 6(2), 12-22. https://doi. org/10.4018/ijesma.2014040102
  • Moreno, J. L. (1953). Who shall survive? Foundations of sociometry, group psychotherapy and sociodrama (2nd ed.). Beacon House. Retrieved from https://psycnet.apa. org/record/1954-04178-000
  • Moreno, J. L. (1955). The sociometric school and the science of man. Sociometry, 18(4), 15-35. https://doi. org/10.2307/2785839
  • Nieto, B. G. (2013). Nuevos espacios comunicativos para las organizaciones: Las redes sociales [New communicative spaces for the organizations: The social networks]. Historia y Comunicacion Social, 18, 731-741. (In Spanish). Retrieved from https:// pdfs.semanticscholar.org/d4bd/ acea5fc283d1e71803534976334f- 0936dae7.pdf
  • Niu, Y., Zhu, X., Li, J., & Hirst, G. (2005). Analysis of polarity information in medical text. Proceedings of the American Medical Informatics Association 2005 Annual Symposium (pp. 570-574). Retrieved from https://www.ncbi.nlm.nih.gov/pmc/ articles/PMC1560818/
  • Nouwens, M., Griggio, C. F., & Mackay, W. E. (2017). "WhatsApp is for family; Messenger is for friends:" Communication places in app ecosystems. Proceedings of the 2017 CHI conference on human factors in computing systems (pp. 727-735). https:// doi.org/10.1145/3025453.3025484
  • Okamoto, K., Chen, W., & Li, X. Y. (2008). Ranking of closeness centrality for large-scale social networks. In International workshop on frontiers in algorithmics (pp. 186- 195). Berlin, Heidelberg: Springer. https://doi.org/10.1007/978-3-540- 69311-6_21
  • Ozili, P. K., & Nanez Alonso, S. L. (2024). Central bank digital currency adoption challenges, solutions, and a sentiment analysis. Journal of Central Banking Theory and Practice, 13(1), 133-165. https://doi. org/10.2478/jcbtp-2024-0007
  • Piatak, J., & Mikkelsen, I. (2021). Does social media engagement translate to civic engagement offline? Nonprofit and Voluntary Sector Quarterly, 50(5), 1079-1101. https:// doi.org/10.1177/0899764021999444
  • Rossmann, A. (2018). Digital maturity: Conceptualization and measurement model. Thirty Ninth International Conference on Information Systems. San Francisco, CA, USA. Retrieved from https:// www.researchgate.net/publication/ 345760193_Digital_Maturity_ Conceptualization_and_Measurement_ Model
  • Singh, J., Singh, G., & Singh, R. (2017). Optimization of sentiment analysis using machine learning classifiers. Human-centric Computing and Information Sciences, 7, Article 32. https://doi.org/10.1186/ s13673-017-0116-3
  • Stephenson, K., & Zelen, M. (1989). Rethinking centrality: Methods and examples. Social Networks, 11(1), 1-37. https://doi.org/10.1016/0378- 8733(89)90016-6
  • Sudha, M., & Sheena, K. (2020). Impact of influencers in consumer decision process: The fashion industry. Interdisciplinary Journal on Law, Social Sciences and Humanities, 1(2), 1-13. http://dx.doi.org/10.19184/ijl. v1i1.19146
  • Sun, K., Wang, H., & Zhang, J. (2022). The impact factors of social media users' forwarding behavior of Covid-19 vaccine topic: Based on empirical analysis of Chinese Weibo users. Frontiers in Public Health, 10. https://doi.org/10.3389/ fpubh.2022.871722
  • The Financial Brand. (2022). Social Media Trends for Q1 2022: Top Banks Losing Facebook Followers. Retrieved from https:// thefinancialbrand.com/news/ social-media-banking/social-mediatrends- bank-facebook-likes-up-ascredit- unions-struggle-154079/
  • Veissi, I. (2017). Influencer marketing on Instagram (Master's Thesis). Haaga-Helia University of Applied Sciences. Retrieved from https://www.theseus.fi/handle/ 10024/135448
  • Wasserman, L., & Kass, R. E. (1995). A reference Bayesian test for nested hypotheses and its relationship to the Schwarz criterion. Journal of the American Statistical Association, 90(431), 928-934. https://doi. org/10.2307/2291327
  • Williams, J. R. (2019). The use of online social networking sites to nurture and cultivate bonding social capital: A systematic review of the literature from 1997 to 2018. New Media & Society, 21(11-12), 2710-2729. https://doi. org/10.1177/1461444819858749
  • Witten, I. H., Frank, E., & Hall, M. A. (2011). Data mining: Practical machine learning tools and techniques. Elsevier.
  • Xu, J., Hu, Zh., Zou, Zh., Zou, J., Hu, X., Liu, L., & Zheng, L. (2020). Design of smart unstaffed retail shop based on IoT and artificial Intelligence. IEEE Access, 8, 147728- 147737. https://doi.org/10.1109/ACCESS. 2020.3014047
  • Zhang, J., & Luo, Y. (2017). Degree centrality, betweenness centrality, and closeness centrality in social network. IProceedings of the 2017 2nd International Conference on Modelling, Simulation and Applied Mathematics (MSAM2017) (pp. 300- 303). Atlantis Press. Retrieved from https://www.atlantis-press.com/ proceedings/msam-17/25874733