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Sentiment analysis is crucial for deciphering customers’ enthusiasm, frustration, and the market mood within the banking sector. This importance arises from financial data’s specialized and sensitive nature, enabling a deeper understanding of customer sentiments. In today’s digital and social marketing landscape within the banking and financial sector, sentiment analysis is significant in shaping customer insights, product development, brand reputation management, risk management, customer service improvement, fraud detection, market research, compliance regulations, etc. This paper introduces a novel approach to sentiment analysis in the banking sector, emphasizing integrating diverse text features to enable dynamic analysis. This proposed approach aims to assess the sentiment score of distinct words used within a document and classify them as positive, negative, or neutral. After rephrasing sentences using synonyms and antonyms of unique words, the system calculates sentence similarity using a distance control mechanism. Then, the system updates the dataset with the positive, negative, and neutral labels. Ultimately, the ELECTRA model utilizes the self-trained sentiment-scored data dictionary, and the newly created dataset is processed using the SoftMax activation function in combination with a customized ADAM optimizer. The approach’s effectiveness is confirmed through the analysis of post-bank customer feedback and the phrase bank dataset, yielding accuracy scores of 92.15%and 93.47%, respectively. This study stands out due to its unique approach, which centers on evaluating customer satisfaction and market sentiment by utilizing sentiment scores of words and assessing sentence similarities.
Mohanty et al. (Sun,) studied this question.