Empowering E-commerce: Leveraging Open AI and Sentiment Analysis for Smarter Recommendations

Online product reviews are pivotal in shaping consumer purchasing decisions in today's digital era. Leveraging the wealth of sentiment-rich data available through these reviews, this research proposes an approach to enhance product recommendation systems. This study integrates sentiment analysis techniques into the recommendation process to provide users with more personalized and insightful product recommendations. By analyzing the sentiment expressed in user-generated content, such as reviews and ratings, this system aims to capture not only the explicit preferences but also the underlying sentiments and emotions of users towards products. Furthermore, this system utilizes OpenAI and the power of Langchain to develop a chatbot interface, enabling users to interact naturally and receive personalized product recommendations based on their preferences and sentiment analysis. Through experimentation on real-world datasets, this paper evaluates the effectiveness and performance of the sentiment-enhanced recommendation system compared to traditional recommendation methods. The results demonstrate the potential of sentiment analysis in improving the relevance, accuracy, and user satisfaction of product recommendations.