E-Commerce Product Review Analysis Using Machine Learning Techniques

Online reviews have grown in importance as a deciding element for shoppers. In order to derive insightful conclusions, this research analyses product reviews from e-commerce websites using machine learning algorithms. The reviews dataset for products in various categories, such as electronics, home appliances, books, etc., will be gathered from well-known e-commerce websites such as Amazon and Flipkart. On the review text, preprocessing techniques including cleaning, tokenization, and lemmatization will be used. To prepare the dataset for training machine learning models, features including review length, rating, date, and user information will be retrieved. The dataset will be used to train models for tasks including sentiment analysis, review rating prediction, and review summarization. These models will be developed using algorithms like logistic regression, random forest, and neural networks. Metrics including accuracy, F1-score, and RMSE will be used to assess the models. Additionally, important facets of model interpretability will be examined. This research will offer valuable insights from data on product reviews that can assist e-commerce companies in better understanding customer happiness and enhancing goods and services.