Network Extraction and Analysis of Character Relationships in Chinese Literary Works.

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  • Author(s): Fan C;Fan C;Fan C; Li Y; Li Y; Li Y
  • Source:
    Computational intelligence and neuroscience [Comput Intell Neurosci] 2022 May 14; Vol. 2022, pp. 7295834. Date of Electronic Publication: 2022 May 14 (Print Publication: 2022).
  • Publication Type:
    Journal Article
  • Language:
    English
  • Additional Information
    • Source:
      Publisher: Hindawi Pub. Corp Country of Publication: United States NLM ID: 101279357 Publication Model: eCollection Cited Medium: Internet ISSN: 1687-5273 (Electronic) NLM ISO Abbreviation: Comput Intell Neurosci Subsets: MEDLINE
    • Publication Information:
      Original Publication: New York, NY : Hindawi Pub. Corp.
    • Subject Terms:
      Asian People*; China ; Cluster Analysis ; Humans
    • Abstract:
      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.
      Competing Interests: The authors declare that they have no conflicts of interest.
      (Copyright © 2022 Chao Fan and Yu Li.)
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    • Publication Date:
      Date Created: 20220524 Date Completed: 20220525 Latest Revision: 20221207
    • Publication Date:
      20240105
    • Accession Number:
      PMC9124099
    • Accession Number:
      10.1155/2022/7295834
    • Accession Number:
      35607464