Abstract

This research explores the integration of web crawling techniques and machine learning algorithms for sentiment analysis in the context of product reviews. With the exponential growth of e-commerce platforms and user-generated content, understanding consumer sentiments towards products has become increasingly valuable for businesses. The study presents an innovative approach that combines Selenium-based web crawling to gather extensive product reviews from online sources and leverages a Random Forest Classifier for sentiment analysis. The research methodology involves data collection, preprocessing, model training, and sentiment prediction. Through this combined approach, the study demonstrates the efficacy of the model in categorizing sentiments within a diverse range of product reviews. The paper discusses the significance of this approach in aiding businesses to comprehend customer feedback at scale, facilitating informed decision-making and enhancing user experience. The findings underscore the potential of this methodology to extract valuable insights from vast amounts of unstructured data available on online platforms.