Utilizing Sentiment Analysis to Enhance Customer Feedback Systems in Banking

The banking industry faces challenges in effectively capturing and analyzing customer feedback, particularly in identifying the true sentiments and opinions of customers. Conventional feedback systems often lack accuracy and are slow to respond to customer needs and complaints, which can negatively impact customer satisfaction and loyalty. To address this issue, this study aims to enhance customer feedback systems by utilizing sentiment analysis, a natural language processing (NLP) technique capable of categorizing customer opinions as positive, negative, or neutral. The research method involves collecting customer feedback data from various sources such as social media, surveys, and online reviews. This data is then analyzed using sentiment analysis algorithms designed to identify emotional patterns in the text. The results indicate that the use of sentiment analysis significantly improves the accuracy and efficiency of customer feedback processing, enabling banks to provide faster and more relevant responses to customer needs. In conclusion, sentiment analysis can significantly strengthen customer feedback systems in the banking sector, offering banks better insights into customer sentiments in real-time. This ultimately contributes to improved customer satisfaction and loyalty, as well as enhancing the quality of services provided.