Sentiment Classification Of E-Commerce Feedback Using Machine Learning Algorithms

In the swiftly changing landscape of e-commerce, it is essential for businesses to comprehend consumer sentiment to improve customer satisfaction and effectively adapt to market changes. An assessment was conducted on the performance of three algorithms for machine learning Support Vector Machines (SVM), Logistic Regression, and Naive Bayes for sentiment analysis of user-generated content on Twitter. A thorough methodology was implemented, including gathering data, preprocessing, extracting features, and evaluating the model to assess how well these methods work. Methods like TFIDF and n-grams enhanced the model's understanding of contextual sentiment. SVM exhibited high accuracy, attributed to its exceptional capability to manage high-dimensional data and establish robust decision boundaries. Nonetheless, challenges persist in interpreting neutral sentiments and ambiguous language. The investigation into complex deep learning methods, encompassing recurrent Transformers and neural networks, presents opportunities for additional advancements in sentiment classification. These findings underscore the significance of sentiment analysis in assisting e-commerce platforms like Amazon, Flipkart, and Meesho in refining their product offerings and marketing strategies, ultimately fostering increased customer engagement and satisfaction.