BIG DATA SENTIMENT ANALYSIS ON PRODUCT REVIEWS

Abstract

The rapid growth of e-commerce platforms has resulted in a massive amount of customer product reviews being generated every day. These reviews contain valuable insights regarding customer satisfaction, product quality, and user experience. However, manually analyzing large volumes of reviews is difficult and time-consuming. This research proposes a Big Data Sentiment Analysis framework for product reviews using machine learning techniques. The system collects product review data and processes it using Natural Language Processing (NLP) methods including tokenization, stop- word removal, and text normalization. Feature extraction is performed using TF-IDF representation, and classification models such as Logistic Regression, Naïve Bayes, and. Random Forest are used to identify the sentiment of reviews as positive, negative, or neutral.
Experimental results show that the proposed system effectively analyzes large-scale review data and achieves high classification accuracy. The framework helps businesses understand customer opinions and improve product quality and customer satisfaction