论文标题
电影中的对象：初始分析
Tropes in films: an initial analysis
论文作者
论文摘要
TVTropes是一种描述Tropes的Wiki，并且使用了哪些艺术作品。我们对胶片非常感兴趣，因此在释放了从本网站中提取数据的tropescraper python模块之后，在本报告中，我们使用刮擦信息来描述统计上的比喻和电影如何相互关联以及这些关系如何演变。为此，我们于2020年4月通过工具对tropscraper生成了一个数据集。我们将其与DB Tropes的最新快照进行了比较，DB Tropes的快照，一个涵盖同一站点并于2016年7月发布的数据集，提供了描述性分析，提供了基本差异，并解决了Wiki在Wiki的演化中，以tropes和Tropes的数字，数字和连接数字，数量，数字，数量，数字，数量，数字，数量，数字，数量，数量，数量，数量，数量，数量，数量，数量，数量，数量，数量，数量，数量，数量，数量。结果表明，比喻和电影的数量使它们的价值翻了一番，并使他们的关系增加了三倍，并且胶片总的来说，用比喻来更好地描述。但是，尽管多年来，比喻最多的电影的类型并没有发生重大变化，但最受欢迎的比喻列表。这种结果可以有助于阐明人们的流行方式如何发展，哪些人变得更受欢迎或消失了，总的来说，一组比喻如何代表电影，并且可能是其成功的关键。生成的数据集，提取的信息以及所提供的摘要都是任何涉及电影和比喻的研究的有用资源。他们可以提供有关模型在数据集顶部建立的模型的行为的适当上下文和解释，包括生成新内容或在机器学习中使用。
TVTropes is a wiki that describes tropes and which ones are used in which artistic work. We are mostly interested in films, so after releasing the TropeScraper Python module that extracts data from this site, in this report we use scraped information to describe statistically how tropes and films are related to each other and how these relations evolve in time. In order to do so, we generated a dataset through the tool TropeScraper in April 2020. We have compared it to the latest snapshot of DB Tropes, a dataset covering the same site and published in July 2016, providing descriptive analysis, studying the fundamental differences and addressing the evolution of the wiki in terms of the number of tropes, the number of films and connections. The results show that the number of tropes and films doubled their value and quadrupled their relations, and films are, at large, better described in terms of tropes. However, while the types of films with the most tropes has not changed significantly in years, the list of most popular tropes has. This outcome can help on shedding some light on how popular tropes evolve, which ones become more popular or fade away, and in general how a set of tropes represents a film and might be a key to its success. The dataset generated, the information extracted, and the summaries provided are useful resources for any research involving films and tropes. They can provide proper context and explanations about the behaviour of models built on top of the dataset, including the generation of new content or its use in machine learning.
