Aspect-Based Sentiment Analysis for Enhancing Customer Satisfaction

Online reviews from customers have grown as a rich source of information for academics and professionals to comprehend the customer experience. This study leverages the rich semantic properties of text reviews to identify hidden and complex areas for product and services improvement. Moreover, it proposes a novel machine learning modeling framework that can reliably and efficiently extract consumer opinions and uncover potential interaction effects( across these opinions). Specifically, we create an opinion extraction framework that makes it possible to determine the average effects of customer opinions.The main goal of this work is to extract and analyze certain aspects—such as pricing, customer service, and product quality. This is done in order to obtain important insights into the opinions that customers have about many facets of stores and dealers. This process involves aspect extraction and sentiment classification to determine positive, negative, or neutral sentiments associated with each aspect. By effectively analysing the sentiments expressed by customers, this work seeks to offer dealers valuable understanding, enabling them to make datadriven decisions for enhancing customer satisfaction and overall business success. This methodology proves a new approach using sentiment analysis and business intelligence that can be applied to any service and product industry.