Unveiling Audience Preferences: A Big Data Analysis of IMDb Movie Ratings and Trends

Audience sentiment is an important driver in the entertainment business, but traditional methods of survey-based research are generally not scalable or objective. Big data analytics have changed this picture, and movie ratings available from the public serve as a good proxy for knowing audience sentiment and engagement. Textual reviews, however, are missing in most datasets, and this restricts traditional sentiment analysis. This work discusses almost 1 million of IMDb movie ratings within 17 genres based on rating-based sentiment classification. Our results show a decreasing trend in ratings over time, different sentiment distributions across genres, and prominent engagement patterns. These findings show the potential of organized big data in supporting decision-making for content creators, marketers, and streaming platforms.