Abstract

Customer feedback and reviews are rich sources of information that reflect the sentiments and experiences of consumers, especially in the financial sector. Mining customer sentiments from these textual data sources provides valuable insights for improving services, identifying emerging issues, and predicting customer satisfaction. This paper proposes a novel approach to mining customer sentiments from financial feedback and reviews, leveraging advanced natural language processing (NLP) techniques, sentiment analysis algorithms, and machine learning models. We discuss methods for preprocessing financial text data, feature extraction, sentiment classification, and visualization of sentiment trends. Additionally, we explore the application of sentiment analysis in understanding customer behaviour, improving financial product offerings, and enhancing customer support strategies. Experimental results demonstrate the effectiveness of the proposed methods in accurately predicting customer sentiments and generating actionable insights. This paper also suggests directions for future research, focusing on real-time sentiment monitoring and integration with personalized financial services