Machine Learning Optimization for Sentiment Analysis in Movie Reviews Based on YouTube Comments
Parminder Singh, Saurabh Dhayani · 2024
This study offers an optimization for sentiment analysis in YouTube movie reviews that is driven by machine learning. The goal of the project is to create a solid model for sophisticated sentiment categorization by utilizing natural language processing and sentiment analysis methods. The technique includes extracting and preprocessing YouTube comments, applying cutting-edge machine learning algorithms, and feature engineering to obtain pertinent sentiment indicators. Improved precision and effectiveness in deciphering the range of emotions expressed in movie-related remarks are promised by the refined model. Results add to the body of knowledge on sentiment analysis research by providing insightful information about the potential and difficulties unique to YouTube movie reviews with accuracy 92%. For researchers, film studios, and content producers looking for a more nuanced understanding of audience attitudes within the vast expanse of YouTube comments, the suggested model is a potent tool.