Analysis of Adapted Films and Stories Based on Social Network

Chowdhury, Tapan; Muhuri, Samya; Chakraborty, Susanta Kumar; Chakraborty, Sabitri Nanda

doi:10.1109/tcss.2019.2931721

Cited by 19 publications

(6 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…With the character recognition method [50], our work will contribute to its promising performance, provide a good benchmark that can assist investigations of intelligent heritage in historical document images. Among with methods that identify and analyse other types of relationships, it will assist extracting knowledge from films because adaptation of a film is the transfer of a novel to its visual medium [50]- [52]. We believe that in the next few years NLP will be a promising technology and extract more interesting results from huge volumes of works of literature in human history.…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2020

View full text Add to dashboard Cite

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.

show abstract

Section: Discussionmentioning

confidence: 99%

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

et al. 2020

View full text Add to dashboard Cite

show abstract

“…They applied it to two Bengali dramas and successfully found the protagonist and antagonist in the literature. Chowdhury et al [ 6 ] investigated character relationships in a novel and its adapted film based on social network. They exploited many network features such as centrality metrics to analyze the difference in the character categorization within two expressions of art.…”

Section: Related Workmentioning

confidence: 99%

Network Extraction and Analysis of Character Relationships in Chinese Literary Works

Fan

Liu

2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

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.

show abstract

“…In this study, we use lingual character networks proposed by Lee and Jung [9]. This model estimates the proximity, based on dialogue frequency between characters, similar to the conventional models [3,15,24,40]. However, it can measure the proximity more accurately by considering that a scene can contain multiple conversations.…”

Section: Character Network and Plot Structuresmentioning

confidence: 99%

Plot Structure Decomposition in Narrative Multimedia by Analyzing Personalities of Fictional Characters

2021

View full text Add to dashboard Cite

This study aims to decompose plot structures of stories in narrative multimedia (i.e., creative works that contain stories and are distributed through multimedia). Since a story is interwoven with main plots and subplots (i.e., primary and ancillary story lines), decomposing a story into multiple story lines enables us to analyze how events in the story are allocated and logically connected. For the decomposition, the existing studies employed character networks (i.e., social networks of characters that appeared in a story) and assumed that characters’ social relationships are consistent in a story line. However, these studies overlooked that social relationships significantly change around major events. To solve this problem, we attempt to use the changes for distinguishing story lines rather than suffer from the changes. We concentrate on the changes in characters’ social relationships being the result of changes in their personalities. Moreover, these changes gradually proceed within a story line. Therefore, we first propose features for measuring changes in personalities of characters: (i) Degrees of characters in character networks, (ii) lengths of dialogues spoken by characters, and (iii) ratios of out-degrees for in-degrees of characters in character networks. We supposed these features reflect importance, inner/outer conflicts, and activeness of characters, respectively. Since characters’ personalities gradually change in a story line, we can suppose that the features also show gradual story developments in a story line. Therefore, we conduct regression for each feature to discover dominant tendencies of the features. By filtering scenes that do not follow the tendencies, we extract a story line that exhibits the most dominant personality changes. We can decompose stories into multiple story lines by iterating the regression and filtering. Besides, personalities of characters change more significantly in major story lines. Based on this assumption, we also propose methods for discriminating main plots. Finally, we evaluated the accuracy of the proposed methods by applying them to the movies, which is one of the most popular narrative multimedia.

show abstract

Analysis of Adapted Films and Stories Based on Social Network

Cited by 19 publications

References 42 publications

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

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

Network Extraction and Analysis of Character Relationships in Chinese Literary Works

Plot Structure Decomposition in Narrative Multimedia by Analyzing Personalities of Fictional Characters

Contact Info

Product

Resources

About