Sentiment Analysis Based on Machine Learning Algorithms: Application to Amazon Product Reviews

Sentiment analysis plays a crucial role in understanding customers’ opinions and sentiments towards products, making it valuable for businesses to make informed decisions. In this article, we present a comprehensive comparative analysis of sentiment analysis techniques applied to Amazon product reviews. Specifically, we employ three popular machine learning algorithms: Logistic Regression, Support Vector Machines (SVM), and Random Forest. Our study focuses on evaluating the performance of these algorithms in terms of accuracy, precision, recall, and F1 score for sentiment classification. We utilize a carefully curated dataset of Amazon product reviews, covering a diverse range of products and customer sentiments. Through extensive experimentation and analysis, we compare the strengths and weaknesses of each algorithm in capturing sentiment information from the reviews. The findings of our study provide valuable insights into the effectiveness of Logistic Regression, SVM, and Random Forest in sentiment analysis of Amazon product reviews. This comparative analysis contributes to the existing literature by shedding light on the performance variations among these algorithms and offering guidance on their application in similar domains.