Building Character Graphs and Dividing Communities in Chinese Novels Based on Graph Data Extraction: Community Division for Character Emotional Polarity Networks

Abstract

Chinese authors indirectly portray real social relationships in society by incorporating their own experiences and feelings about politics, economic life, and cultural habits. Social and natural knowledge are mixed and hidden in natural literary language. Therefore, by well defining the type of original text, the pipeline for processing the text and the rules to build the graph, we can extract enough valid and useful information. Based on higher-level information, we can build a literary character graph to promote computers' comprehension of literature. The identification of opposite characters is a challenging topic because the character relationships are intricate and complex. The cryptic expression in literary works can enhance the readability of the plot, but it is obvious that it increases the cost of understanding. Even for human readers different types of characters are difficult to classify and identify, and it usually needs careful reading for several times to get the subtle differences among the characters. Though extracting knowledge from Chinese literature is a complicated and difficult topic, this paper models the graph of polar literary characters and divides polar communities by extracting polar vertices and polar edges based on the concept of literary sentiment polarity. The results show that using a long-sentence window is a good trade-off. The experiments of modern Chinese polar literature show that the accuracy of the community division method for the integrated graph polarity is obviously better than the method based on the co-occurrence network, and it can automatically match the positive and negative communities as well as build the complete graph structure. It clearly reflects the meaning of literary works from the perspective of polarity. This systematic model describes general standard operation steps for the machine to understand complex Chinese literature. The experiments conducted on seven benchmark Chinese novel datasets demonstrate that the method based on emotional polarity shows a significant improvement compared to baseline performance. Though we use Chinese datasets in this paper, the model and methods are important references for literature analysis and graph extraction in other languages. INDEX TERMS Community division, character graphs, emotional polarity, natural language processing, social network analysis.

Network Extraction and Analysis of Character Relationships in Chinese Literary Works

“…Experts in both literature and computer science have recently focused on comprehensive analysis, which will be the main direction for literary research in the future. Zhao et al [ 2 ] established a model of character graphs based on emotional polarity and gave general procedures to comprehend the Chinese literature. Huang et al [ 3 ] developed a Chinese person entity relation extraction technology utilizing distant supervision, which can automatically identify and extract semantic relationships.…”

Distinguishing Oracle Variants Based on the Isomorphism and Symmetry Invariances of Oracle-Bone Inscriptions

“…the basis of oracle-bone inscriptions, there are still big differences between the two types of character systems. Oraclebone inscriptions are the starting point of the development of Chinese characters, where social and natural knowledge were mixed [2]. Although they are systematic, the shape and structure of the characters are not fixed, and the writing style of ancient oracle-bone inscriptions is not standardized.…”