Customer Feedback Analysis Using Aspect Based Sentiment Analysis and Fuzzy Analytic Hierarchy Process

Customer feedback analysis has become crucial in the area of business analytics in recent years. Proper assessment of customers’ perception of products and services helps organizations make data-driven decisions and maintain a competitive edge. In this article, we have developed a decision support framework to help consumers make informed choices, using Natural Language Processing (NLP) on customer feedback. Our proposed technique first uses Aspect-based Sentiment Analysis (ABSA)to identify key features and the corresponding sentiments. We have used Bi-directional Encoder Representations from Transformers (BERT) to derive sentiment scores for different criteria across different alternatives. Then, a multi-criteria decision-making (MCDM) technique such as Fuzzy Analytical Hierarchy Process (FAHP) is used for ranking of preferred alternatives. The framework is demonstrated using a case study on customer opinions shared on social media about several airlines.