Sentiment-driven Movie Recommendation System: A Machine Learning Approach

Abstract

Pioneering method for enhancing movie recommendation systems through the integration of sentiment analysis of micro-blogging data. Leveraging natural language processing (NLP) techniques, the proposed system extracts sentiment from tweets and other micro-blogging sources pertinent to movies systematic approach encompassing the collection, preprocessing, and analysis of movie review data tailored for sentiment analysis tasks. Key aspects covered include the acquisition of publicly available datasets, methodologies for web scraping, preprocessing techniques, strategies for sentiment labeling, methods for data augmentation, procedures for data splitting, optimal data storage formats, and ethical considerations inherent in data collection and utilization. By offering a comprehensive guide, furnish both researchers and practitioners with the necessary tools to proficiently manage movie review data while navigating the ethical intricacies associated with its acquisition and application.