Purpose

The purpose of this paper is to develop a predictive model for box office performance based on the textual information in movie scripts in the green-lighting process of movie production.

Design/methodology/approach

The authors use Latent Dirichlet Allocation to determine the hidden textual structure in movie scripts by extracting topic probabilities as predictors for classification. The extracted topic probabilities are used as inputs for the predictive model for the box office performance. For the predictive model, the authors utilize a variety of classification algorithms such as logistic classification, decision trees, random forests, k-nearest neighbor algorithms, support vector machines and artificial neural networks, and compare their relative performances in predicting movies' market performance.

Findings

This approach for extracting textual information from movie scripts produces a valuable typology for movies. Moreover, our modeling approach has significant power to predict movie scripts' profitability. It provides a superior prediction performance compared to previous benchmarks, such as that of Eliashberg et al. (2007).

Research limitations/implications

This work contributes to literature on predicting the box office performance in the green-lighting process and literature regarding suggesting models for the idea screening stage in the new product development process. Besides, this is one of the few studies that use movie script data to predict movies' financial performance by proposing an approach to integrate text mining models and machine learning algorithms with movie experts' intuition.

Practical implications

First, the authors’ approach can significantly reduce the financial risk associated with movie production decisions before the pre-production stage. Second, this paper proposes an approach that is applicable at a very early stage of new product development, such as the idea screening stage. The authors also introduce an online-based movie scenario database system that can help movie studios make more systematic and profitable decisions in the green-lighting process. Third, this approach can help movie studios estimate movie scripts' financial value.

Originality/value

This study is one of the few studies to forecast market performance in the green-lighting process.

中文翻译：

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从直觉到智能：一种基于文本挖掘的电影绿灯过程方法

目的

本文的目的是开发一种基于电影制作过程中电影剧本中的文本信息的票房表现预测模型。

设计/方法/方法

作者使用潜在狄利克雷分配通过提取主题概率作为分类预测变量来确定电影剧本中隐藏的文本结构。提取的主题概率用作票房表现预测模型的输入。对于预测模型，作者利用了多种分类算法，如逻辑分类、决策树、随机森林、k-最近邻算法、支持向量机和人工神经网络，并比较了它们在预测电影市场表现方面的相对表现。

发现

这种从电影剧本中提取文本信息的方法产生了一种有价值的电影类型学。此外，我们的建模方法在预测电影剧本的盈利能力方面具有重要作用。与之前的基准（例如 Eliashberg等人的基准）相比，它提供了更好的预测性能。(2007)。

研究限制/影响

这项工作有助于预测绿灯过程中票房表现的文献以及有关为新产品开发过程中的创意筛选阶段建议模型的文献。此外，这是为数不多的使用电影剧本数据来预测电影财务表现的研究之一，它提出了一种将文本挖掘模型和机器学习算法与电影专家的直觉相结合的方法。

实际影响

首先，作者的方法可以在预制作阶段之前显着降低与电影制作决策相关的财务风险。其次，本文提出了一种适用于新产品开发早期阶段的方法，例如创意筛选阶段。作者还介绍了一个基于在线的电影场景数据库系统，可以帮助电影制片厂在绿灯过程中做出更系统和更有利可图的决策。第三，这种方法可以帮助电影制片厂估算电影剧本的财务价值。

原创性/价值

这项研究是为数不多的预测绿色照明过程中市场表现的研究之一。