The School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214122, China.
Jiangsu Key Laboratory of Media Design and Software Technology, Jiangnan University, Wuxi 214122, China.
Comput Intell Neurosci. 2022 May 14;2022:7295834. doi: 10.1155/2022/7295834. eCollection 2022.
Character relationships in literary works can be interpreted and analyzed from the perspective of social networks. Analysis of intricate character relationships helps to better understand the internal logic of plot development and explore the significance of a literary work. This paper attempts to extract social networks from Chinese literary works based on co-word analysis. In order to analyze character relationships, both social network analysis and cluster analysis are carried out. Network analysis is performed by calculating degree distribution, clustering coefficient, shortest path length, centrality, etc. Cluster analysis is used for partitioning characters into groups. In addition, an improved visualization method of hierarchical clustering is proposed, which can clearly exhibit character relationships within clusters and the hierarchical structure of clusters. Finally, experimental results demonstrate that the proposed method succeeds in establishing a comprehensive framework for extracting networks and analyzing character relationships in Chinese literary works.
文学作品中的角色关系可以从社交网络的角度进行解释和分析。分析复杂的角色关系有助于更好地理解情节发展的内在逻辑,并探索文学作品的意义。本文尝试基于共词分析从中国文学作品中提取社交网络。为了分析角色关系,同时进行了社交网络分析和聚类分析。通过计算度分布、聚类系数、最短路径长度、中心性等来进行网络分析。聚类分析用于将角色划分为不同的组。此外,还提出了一种改进的层次聚类可视化方法,可以清晰地展示簇内的角色关系以及簇的层次结构。最后,实验结果表明,所提出的方法成功地建立了一个全面的框架,用于提取中国文学作品中的网络和分析角色关系。