Bank Customer Complaints Analysis Using Natural Language Processing and Data Mining


  • Nikitha G N
  • Chandana C
  • Neelashree N
  • Nisargapriya J
  • Vishwesh J


t-SNE(t-Distributed Stochastic Neighbour Embedding), LDA(Latent Dirichlet Allocation), NLP(Natural language processing ).


The banking sector has undergone a major revolution with the advent of digital transformation. The entry of Fintech and tech giants such as Google, Amazon, and Facebook have introduced convenient banking that is easy to understand and use. In this competitive environment, banks are realizing the importance of customer service and satisfaction and want to pay close attention to the Voice of Customer to improve the customer experience. By analyzing and getting insights from customer feedback, banks will have better information to make strategic decisions. In their quest to better understand their customers, banks are seeking artificial intelligence (AI) solutions in the form the of sentiment analysis. What is sentiment analysis? In simple words, sentiment analysis is the process of detecting a customer’s reaction to a product, brand, situation or event through texts, posts, reviews, and other digital content. Using sentiment analysis, business leaders can gain deep insight into how their customers think and feel. The analysis can help in tracking customer opinions over a period of time, determine customer segmentation, plan product improvements, prioritize customer service issues, and many more business use cases.


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How to Cite

Nikitha G N, Chandana C, Neelashree N, Nisargapriya J, & Vishwesh J. (2020). Bank Customer Complaints Analysis Using Natural Language Processing and Data Mining . International Journal of Progressive Research in Science and Engineering, 1(3), 22–25. Retrieved from