Abstract
Sentiment analysis serves as a crucial approach for gauging public opinion across various sectors, including the realm of product reviews. This study focuses on the evaluation of customer sentiments in Amazon reviews using an array of machine learning algorithms—Logistic Regression, Random Forest, Convolutional Neural Networks (CNN), and Long Short-Term Memory (LSTM) networks. The methodology employed is robust, characterized by meticulous parameter tuning based on both theoretical and empirical considerations. Comparative analysis of these algorithms, grounded in accuracy and other performance metrics, offers valuable insights into their respective efficacies and limitations for sentiment analysis tasks. The findings of this study contribute to an enhanced understanding of the performance of different machine learning algorithms in sentiment classification and provide a foundation for future research in this domain.
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