Unveiling Emotions: Exploring Sentiment Analysis on Flipkart Data with the Power of Support Vector Machines and the Intricacies of Random Forest Algorithm

E-commerce platforms have reshaped consumer shopping, emphasizing customer experience and satisfaction. This study compares sentiment analysis techniques employing Support Vector Machine (SVM) and Random Forest Algorithms on a Flipkart dataset. User-authored reviews and ratings are analyzed to categorize sentiments as positive, negative, or neutral. The research evaluates data pre-processing methods and measures their efficacy through accuracy, precision, recall, and F-1 score. Findings reveal Random Forest showing a slight advantage over SVM with a marginal deviation of 0.43%, where their individual accuracy rates provision are 94.03% and 93.60% respectively. These results highlight the strengths and limitations of each approach, providing valuable insights for businesses to enhance customer satisfaction and drive growth in the fiercely competitive e-commerce landscape.