Optimizing Storytelling, Improving Audience Retention, and Reducing Waste in the Entertainment Industry
This work addresses financial risk in programming decisions for television networks, offering incremental improvements through data-driven insights.
This study tackled the problem of forecasting television viewership by introducing a machine learning framework that integrates NLP features from episode dialogue with traditional data, finding that NLP features provide meaningful improvements for some series.
Television networks face high financial risk when making programming decisions, often relying on limited historical data to forecast episodic viewership. This study introduces a machine learning framework that integrates natural language processing (NLP) features from over 25000 television episodes with traditional viewership data to enhance predictive accuracy. By extracting emotional tone, cognitive complexity, and narrative structure from episode dialogue, we evaluate forecasting performance using SARIMAX, rolling XGBoost, and feature selection models. While prior viewership remains a strong baseline predictor, NLP features contribute meaningful improvements for some series. We also introduce a similarity scoring method based on Euclidean distance between aggregate dialogue vectors to compare shows by content. Tested across diverse genres, including Better Call Saul and Abbott Elementary, our framework reveals genre-specific performance and offers interpretable metrics for writers, executives, and marketers seeking data-driven insight into audience behavior.