Genre plays an important role in the description, navigation, and discovery of movies, but it is rarely studied at large scale using quantitative methods. This allows an analysis of how genre labels are applied, how genres are composed and how these ingredients change, and how genres compare. We apply unsupervised topic modeling to a large collection of textual movie summaries and then use the model's topic proportions to investigate key questions in genre, including recognizability, mapping, canonicity, and change over time. We find that many genres can be quite easily predicted by their lexical signatures and this defines their position on the genre landscape. We find significant genre composition changes between periods for westerns, science fiction and road movies, reflecting changes in production and consumption values. We show that in terms of canonicity, canonical examples are often at the high end of the topic distribution profile for the genre rather than central as might be predicted by categorization theory.

中文翻译：

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使用情节摘要的主题模型对电影进行类型分析

类型在电影的描述、导航和发现中起着重要作用，但很少使用定量方法对其进行大规模研究。这允许分析如何应用类型标签、如何组成类型以及这些成分如何变化，以及如何比较类型。我们将无监督主题建模应用于大量文本电影摘要，然后使用模型的主题比例来研究类型中的关键问题，包括可识别性、映射、规范性和随时间的变化。我们发现许多流派可以很容易地通过它们的词汇特征来预测，这定义了它们在流派景观中的位置。我们发现西部片、科幻片和公路片在不同时期之间的流派构成发生了显着变化，反映了生产和消费价值的变化。