Extraction and Analysis of Fictional Character Networks: A Survey
July 05, 2019 ยท The Cartographer ยท ๐ ACM Computing Surveys
"No code URL or promise found in abstract"
"Title-pattern auto-detect: Extraction and Analysis of Fictional Character Networks: A Survey"
Evidence collected by the PWNC Scanner
Authors
Vincent Labatut, Xavier Bost
arXiv ID
1907.02704
Category
cs.SI: Social & Info Networks
Cross-listed
cs.CL,
cs.CV,
cs.IR,
cs.MM
Citations
100
Venue
ACM Computing Surveys
Last Checked
1 day ago
Abstract
A character network is a graph extracted from a narrative, in which vertices represent characters and edges correspond to interactions between them. A number of narrative-related problems can be addressed automatically through the analysis of character networks, such as summarization, classification, or role detection. Character networks are particularly relevant when considering works of fictions (e.g. novels, plays, movies, TV series), as their exploitation allows developing information retrieval and recommendation systems. However, works of fiction possess specific properties making these tasks harder. This survey aims at presenting and organizing the scientific literature related to the extraction of character networks from works of fiction, as well as their analysis. We first describe the extraction process in a generic way, and explain how its constituting steps are implemented in practice, depending on the medium of the narrative, the goal of the network analysis, and other factors. We then review the descriptive tools used to characterize character networks, with a focus on the way they are interpreted in this context. We illustrate the relevance of character networks by also providing a review of applications derived from their analysis. Finally, we identify the limitations of the existing approaches, and the most promising perspectives.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
๐ Similar Papers
In the same crypt โ Social & Info Networks
R.I.P.
๐ป
Ghosted
R.I.P.
๐ป
Ghosted
Fake News Detection on Social Media: A Data Mining Perspective
R.I.P.
๐ป
Ghosted
Heterogeneous Graph Attention Network
R.I.P.
๐ป
Ghosted
Natural Scales in Geographical Patterns
R.I.P.
๐ป
Ghosted
Representation Learning on Graphs: Methods and Applications
R.I.P.
๐ป
Ghosted