“Amazon Product Review Sentiment Analysis” aims to leverage the power of machine learning and natural language processing (NLP) techniques to extract valuable insights from the extensive repository of Amazon product reviews. In the era of e-commerce, where consumer feedback is abundant yet complex to decipher, endeavours to develop an automated system capable of analysing sentiments expressed in Amazon product reviews, providing a comprehensive understanding of customer sentiments towards various products. The vast collection of product reviews on Amazon holds a wealth of information regarding customers' experiences, opinions, and sentiments about products. However, manually sifting through this abundance of data to derive meaningful insights is a daunting task. This seeks to alleviate this challenge by employing sophisticated machine learning models and NLP algorithms to automate the sentiment analysis process. By classifying reviews into positive, negative, or neutral sentiments, this system aims to empower both consumers and sellers on the platform.

OBJECTIVES:

To Create a machine learning or natural language processing model capable of analysing sentiment in Amazon product reviews. To Achieve high accuracy in classifying reviews into positive, negative, or neutral sentiments to provide valuable insights to users. T o Allow users to customize the analysis by specifying products, categories, or time frames for sentiment analysis. Incorporate effective data visualization techniques to present sentiment analysis results in a visually understandable format. To develop my skills in the field of machine learning