Sentiment Analysis: A Machine Learning Perspective

An essential part of natural language processing, sentiment analysis makes it possible to automatically extract sentiment from textual input. In this work, we examine how well different machine learning algorithms perform when used to analyze the sentiment of real-time Amazon reviews in the automobile niche. Leveraging a dataset comprising reviews from Amazon users, we employ SVM, KNN, Logistic Regression, and Random Forest algorithms to categorize text sentiment as positive, negative, or neutral. This work evaluates the performance of each algorithm in sentiment analysis tasks using strict evaluation standards and testing. Our findings provide insights into the effectiveness of machine learning approaches for analyzing sentiment in real-time reviews, offering valuable implications for businesses and organizations aiming to leverage customer feedback for decision-making and user experience enhancement.