用对话分析提升剧集收视预测，帮影视公司降本增效
Optimizing Storytelling, Improving Audience Retention, and Reducing Waste in the Entertainment Industry
- 融合2.5万集剧集对话的NLP特征与收视数据
- 情感基调和叙事结构使预测准确率提升12%以上
- 适合编剧、制片人做内容优化与决策参考
电视网络在节目决策中面临高财务风险，常依赖有限的历史数据预测单集收视率。本研究提出一种机器学习框架，整合超过25000集电视剧的自然语言处理（NLP）特征与传统收视数据，以提升预测准确性。通过提取剧集对白中的情感基调、认知复杂度和叙事结构，采用SARIMAX、滚动XGBoost及特征选择模型评估预测表现。尽管历史收视仍是强基线，但NLP特征对部分剧集有显著改进效果。我们还引入基于对话向量欧氏距离的相似性评分方法，按内容对比剧集。在《风骚律师》《小学风云》等多类型剧集中测试，框架展现类型特异性表现，并为编剧、制片人与营销人员提供可解释的数据洞察。
原文摘要 · Abstract (English)
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.
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