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Network Extraction and Analysis of Character Relationships in Chinese Literary Works
1The School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214122, China.
Computational Intelligence and Neuroscience
|May 24, 2022
Summary
This study extracts social networks from Chinese literature using co-word analysis and network analysis. The findings offer a new framework for understanding complex character relationships and literary structures.
Area of Science:
- Computational Linguistics
- Digital Humanities
- Network Science
Background:
- Character relationships are key to understanding literary works.
- Social network analysis provides a quantitative approach to study these relationships.
Purpose of the Study:
- To extract social networks from Chinese literary works.
- To analyze character relationships using computational methods.
- To develop an improved visualization for hierarchical clustering.
Main Methods:
- Co-word analysis to identify character co-occurrences.
- Social network analysis (SNA) metrics (degree distribution, centrality, etc.).
- Cluster analysis and hierarchical clustering for grouping characters.
Main Results:
- Successfully extracted social networks from Chinese literary texts.
- Quantified character relationships through network and cluster analysis.
- Developed a novel visualization method for hierarchical clustering.
Conclusions:
- The proposed framework effectively extracts and analyzes character relationships in Chinese literature.
- This computational approach enhances literary analysis and understanding of narrative structures.
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