Sentiment Analysis-Based Product Recommendations for E- Commerce Navigation

Abstract

E-commerce websites have revolutionized the shopping process by offering a tremendous amount of products and services but assisting customers to buy the correct products remains a challenge. The traditional recommendation systems tend to overlook details that are significant in the user-created content like reviews. This paper discusses sentiment analysis as a novel approach to enhance product recommendation in e-commerce. As an aspect of natural language processing sentiment analysis takes attitudes and opinions out of textual data to identify customer preferences and areas for improvement in reviews and comments. The development and testing of specialized sentiment analysis algorithms, which deal with such problems as context and language changes, will focus on the e-commerce sphere. Emotion recommendation combined with e-commerce displays tries to offer a more personalized shopping experience to users. The study's conclusions may have a big impact on e-commerce by raising income for online merchant’s customer satisfaction and conversion rates. Moreover, it helps fill in the gap between user sentiment and product recommendations which points to the immense potential of sentiment analysis in recommendation systems. The final aim of this study is to transform online product selection and discovery using sentiment analysis in online shopping.