Leveraging Sentiment Analysis for Enhanced Customer Experience: AI-Driven Personalization in Online Retail

Abstract

This research investigates the potential of sentiment analysis as an artificial Intelligence (AI)-driven technique for enhancing customer experience within the online retail sector. The study explores the application of machine learning algorithms to analyze customer feedback from product reviews, aiming to extract granular sentiment expressed toward specific product features and aspects of the purchasing processes. By comparing the performance of algorithms including Native Bayes, Support Vector Machines (SVM), and Neural Networks under different resampling methods, this research establishes a robust approach to classifying customer sentiments into positive and negative categories. These findings demonstrate the efficacy of these analytical techniques in identifying customer pain points and preferences, which can be strategically used to personalize service delivery and improve overall customer satisfaction in online retail.