Comparative Analysis of Consumer Sentiments using NLP and Pre-Trained Transformer-Based Models

With the rapid development of web-based business or e-commerce, online reviews are crucial in impacting consumer buying choices. Examining sentiment from these reviews is fundamental for organizations to grasp customer feedback and work on their products or administrations. In this research, we lead a relative analysis of Amazon reviews utilizing natural language processing (NLP) methods and pre-trained transformer-based models. We aim to assess the performance of famous models like VADER, RoBERTa, and DistilBERT in sentiment analysis undertakings on a massive dataset of Amazon item surveys. Our review centers around evaluating these models' precision, proficiency, and versatility in scalability, as well as the nuanced sentiments communicated in consumer reviews. By comparing the outcomes, we plan to give bits of knowledge into the effectiveness of various methodologies for sentiment analysis regarding e-commerce reviews, at last helping organizations settle on information-driven choices to improve consumer satisfaction and item quality.