Leveraging Natural Language Processing and Machine Learning for Consumer Insights from Amazon Product Reviews

The number of online product reviews, such as on Amazon, has increased in recent years due to the feature of the increased use of online shopping. The idea behind sentiment analysis, also called opinion mining, is based on recognizing the sentiment or opinion of a particular piece of text and its elements derived from reviews, tweets or customer feedback through elearning, computational linguistics, Machine Learning (ML), and Natural Language Processing (NLP). This study delves into the use of ML and NLP methods to analyze product reviews on Amazon and pull out important attitudes, tastes, and trends. Other measures that are considered while assessing the models, including the BERT, are F1-Score, recall, accuracy, and precision. In sentiment categorization and review analysis, BERT (Bidirectional Encoder Representations from Transformers) outperformed the other methods with 89% accuracy, 88 % precision, 88 % recall, and 89 % F1-score. These outcomes suggest that latest NLP models are fit for efficiently analyzing complex and heterogeneous consumers' response and potential for increasing the value and meeting the customer needs and expectations. The integration of AI (Artificial Intelligence)-based methods explored in this study reveals how consumer feedback analysis could transform in the next years as more firms harness it to drive their decisions and strategies for a changing world.