Temporal and Sentimental Analysis of Customer Reviews

Consumer attitudes are crucial in influencing purchases in the dynamic world of e-commerce. This study conducts a thorough temporal analysis of review emotions obtained from the well-known online retailer in India. The main goal is to ascertain how the emotion conveyed in customer evaluations changes over time. With the use of cutting-edge Natural Language Processing (NLP) methods, we are able to extract and classify the feelings stated in a variety of product evaluations. In this study, data analysis has been conducted using information from an E-commerce website over varying time intervals. The temporal range for the dataset utilized in this research ranges from 2019 to 2023. Interesting trends are emerging from preliminary findings. Positive attitudes increase during seasonal peaks, presumably due to increased consumer fervor. Gaining knowledge about temporal sentiment patterns may be a useful tool for firms when developing products, marketing plans, and consumer interaction programs. Businesses may increase customer happiness and loyalty by matching their products to consumer mood at certain time intervals. This work advances academic discussion on sentiment analysis and consumer behavior by highlighting the significance of taking temporal factors into account.