Sentiment Analysis for Predictive Insights in the Media & Entertainment Industry Using Big Data

Abstract

ABSTRACT

Sentiment analysis, driven by big data analytics, has emerged as a powerful tool for deriving predictive insights in the media and entertainment industry. With the rise of digital platforms, streaming services, and social media, vast amounts of user-generated data are being produced daily, reflecting audience opinions, emotions, and engagement levels. Traditional audience analysis methods are often slow and limited in scope, making it challenging for media companies to predict content performance, optimize marketing strategies, and enhance viewer satisfaction. By leveraging machine learning algorithms and natural language processing (NLP) techniques, sentiment analysis enables real-time interpretation of audience sentiment, helping businesses make data-driven decisions. This study explores how sentiment analysis can forecast trends, assess audience preferences, and improve content personalization for media platforms, film studios, and streaming services. Additionally, it examines the role of big data in sentiment extraction, predictive modeling, and business intelligence. Challenges such as data privacy, bias in sentiment classification, and handling unstructured data are also discussed. This research highlights the transformative potential of sentiment analysis in shaping the future of media and entertainment by enabling companies to anticipate audience needs, refine content strategies, and drive business success through big data analytics.