Analyzing Customer Feedback on Amazon to Reveal Sentiment Trends using Machine Learning

Online reviews on platforms like Amazon provide valuable insights into customer experiences, but manually processing this data is time-consuming and impractical. This paper “Analyzing Customer Feedback on Amazon to Reveal Sentiment Trends using Machine Learning” delves into the providing details of sentiment analysis on Amazon reviews, utilizing machine learning techniques. This paper aims to automate the classification of reviews based on both topic classification (product-based review or service-based review) and sentiment (positive review, negative review, neutral review). This study examines the application of machine learning techniques to analyze customer feedback from Amazon, with a focus on separately classifying product and service reviews, followed by sentiment analysis for each category. The results highlight key trends in customer experiences and demonstrate the effectiveness of machine learning in multi-level sentiment analysis. By the use of Naïve Bayes Classifier, the model achieves the accuracy of range 80% to 90%.