ENTERTAINMENT OTT CONTENT ANALYTICS AND PREDICTION

Abstract

The rapid growth of Over-The-Top (OTT) platforms has resulted in a large amount of content and user interaction data. OTT platforms provide movies, web series, documentaries, and other digital content across different languages, genres, and regions. Analyzing this data can help understand content popularity, audience preferences, viewing patterns, and platform performance. The proposed Entertainment OTT Content Analytics and Prediction system is designed to analyze OTT content and generate meaningful insights from historical data. The system processes attributes such as content title, genre, language, release year, duration, content type, ratings, number of views, popularity, and audience engagement. Data preprocessing techniques are applied to clean and organize the collected information. The system performs descriptive and exploratory analysis to identify popular genres, languages, content types, release trends, audience ratings, and engagement patterns. Interactive dashboards provide visual representations using charts, graphs, tables, filters, and KPI cards. Users can compare content based on ratings, popularity, release year, genre, language, and other available attributes. The prediction module uses historical OTT data and Machine Learning techniques to estimate content popularity, ratings, or audience engagement. Predictive models can identify relationships between content characteristics and audience response. The system can also incorporate sentiment analysis of user reviews to understand positive, neutral, and negative audience opinions. Overall, the proposed system combines OTT content analytics, audience behavior analysis, visualization, and predictive modeling in a unified platform. It can help content analysts and OTT businesses understand historical performance and audience preferences. Future enhancements can include personalized recommendations, realtime viewing analytics, churn prediction, content demand forecasting, and advanced audience segmentation.