BIG DATA ANALYTICS FOR AUDIENCE SENTIMENT FORECASTING IN FILM PRODUCTION AND DISTRIBUTION - Sharma, Abhinav; Joshi, Sagar Vasantrao; Singh, Saket Kumar; Singh, Anchal; Kharade, Amruta Prasad; Khidse, Swati Vitthal (ShodhKosh: Journal of Visual and Performing Arts, 2025)
As data-driven decisions become more common in film production and marketing, big data analytics has become an important tool for figuring out how people will feel about movies. Sentiment predicting helps directors and producers make better content, marketing plans, and release dates by letting them know how audiences feel and what they like. This essay looks at how big data analytics can be used to predict how people will feel about something. It focusses on how it can be used to look at public feedback from different places, like social media, reviews, and movie theatre performance. The study shows a new way to do mood analysis by using machine learning techniques to look at big sets of data and guess how people will react to pictures. The system uses emotion analysis methods like natural language processing (NLP) and deep learning models to give useful information about the mood, engagement, and possible success of a movie at the box office. The study also talks about how to improve the accuracy of forecasts by combining mood data with past performance data. The method looks at different mood analysis tools, model designs, and evaluation measures, focussing on how well these models can react to different types of content and audience groups. The results show a strong link between mood indicators and how well the audience responded. This shows how big data analytics could change how the entertainment business makes decisions about marketing and production. This study shows how viewer sentiment is becoming more important in film strategy and plans for how sentiment predicting tools will change in the future.