Comparing Machine Learning Algorithms for Sentiment Analysis in Movie Reviews

Sentiment analysis is a crucial natural language processing component that helps businesses to understand public opinion, customer feedback, and market trends. This study evaluates the performance of three machine learning models, namely Naive Bayes, Logistic Regression and SVM in classifying the sentiment of movie reviews. We analyzed 50,000 IMDB reviews from Kaggle using TF-IDF with three ML models using the following key metrics: accuracy, precision, recall, and F1-score. We further analyzed the confusion matrix for each model, which provided insights into the number of correctly and incorrectly classified reviews, helping identify potential areas for improvement. The findings of this study can guide the selection and optimization of algorithms for sentiment analysis, ultimately improving marketing, customer engagement, and social media strategies.