悲剧和喜剧网络基于结构特征的戏剧体裁聚类
Tragic and Comical Networks. Clustering Dramatic Genres According to Structural Properties
摘要：
在网络研究和戏剧史的结合领域中，越来越多的传统是从戏剧的人物网络中产生解释。这种解释的潜力在于，与阅读文本或观看表演相比，图表提供了不同的人物关系表示。我们的目标是创建一种方法，该方法能够在戏剧的可解释性和简单属性的基础上，独立于戏剧中的角色数量，或者换句话说，网络的大小，对具有相似结构的文本进行聚类。发现这些特征是我们研究的最重要部分，以及建立适当的统计程序来计算文本之间的相似性。我们的数据从DraCor数据库下载并在R中进行分析（我们使用GerDracor和ShakeDraCor子集合）。我们希望提出一种鲁棒的方法，基于字符之间的单词分布;场景中的字符分布，语音行为的平均长度，或字符特定的和宏观级别的网络属性，如聚类系数和网络密度。基于这些指标的监督分类程序应用于子集合分类喜剧和悲剧使用支持向量机（SVM）的方法。我们的研究表明，这种方法也可以在小样本量上产生可靠的结果。
英文摘要：
There is a growing tradition in the joint field of network studies and drama history that produces interpretations from the character networks of the plays.The potential of such an interpretation is that the diagrams provide a different representation of the relationships between characters as compared to reading the text or watching the performance. Our aim is to create a method that is able to cluster texts with similar structures on the basis of the play's well-interpretable and simple properties, independent from the number of characters in the drama, or in other words, the size of the network. Finding these features is the most important part of our research, as well as establishing the appropriate statistical procedure to calculate the similarities between the texts. Our data was downloaded from the DraCor database and analyzed in R (we use the GerDracor and the ShakeDraCor sub-collection). We want to propose a robust method based on the distribution of words among characters; distribution of characters in scenes, average length of speech acts, or character-specific and macro-level network properties such as clusterization coefficient and network density. Based on these metrics a supervised classification procedure is applied to the sub-collections to classify comedies and tragedies using the Support Vector Machine (SVM) method. Our research shows that this approach can also produce reliable results on a small sample size.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
