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Abstract: With the rapid growth of e-commerce, the number of online products and customer reviews has increased. As more people share feedback through reviews, extracting meaningful insights from this vast data becomes crucial. Sentiment analysis categorizes these reviews as positive, negative, or neutral, helping to gather essential information. The project employs Support Vector Machines (SVMs), a robust machine learning model known for its effectiveness in text classification and handling large datasets. The process begins with preprocessing unstructured reviews, including tokenization, stop word removal, and stemming or lemmatization, to clean the data. Sentiment polarity is then calculated, and key features are extracted to enhance model accuracy. This analysis provides customers with clear insights into product satisfaction, aiding informed purchasing decisions. For businesses, it offers actionable feedback to improve product quality, monitor brand reputation, and refine offerings based on real customer experiences.