Les réseaux de personnages de science-fiction : échantillons de lectures intermédiaires

Rochat, Yannick; Triclot, Mathieu

doi:10.4000/resf.1183

Cited by 4 publications

(21 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In practice, it is hard to handle more than a few works of fiction when using either of the previous approaches (direct annotation vs. predefined character index). A workaround is to turn to crowdsourcing, as Rochat & Kaplan do in [209] to constitute their own indices. Interestingly, this study is also characterized by its multimedia nature, as the authors consider a corpus of science-fiction works including novels, comics, movies, TV series, and video games.…”

Section: Vincent Labatut and Xavier Bostmentioning

confidence: 99%

“…For instance, Certain authors focus on more sociological traits. In [209], Rochat & Triclot are interested in the representation and relative position of science and politics in science-fiction works. Through crowdsourcing, they manually define an attribute representing whether the main occupation of each character is mainly related to Politics, Technology, or Science, but also Family, Religion, or Art.…”

Section: Attributementioning

confidence: 99%

“…The co-occurrence-based approach is the most widespread in the literature, probably because it is the easiest to apply: detecting interactions in a more precise way can be a difficult problem, even for humans [209]. This approach consists in breaking down the considered work into smaller narrative units, and in assuming that two characters interact when they occur together within the same unit.…”

Section: Co-occurrencesmentioning

confidence: 99%

“…Comics. In comics, Rochat uses a two-page narrative unit [209], like he does for novels (see above). In [8,92], the authors use the comic book issue, but this narrative unit is dictated by the predefined database they use as raw material.…”

Section: Vincent Labatut and Xavier Bostmentioning

confidence: 99%

“…Certain authors directly and explicitly tie the density to some aspects of the narrative. Rochat & Triclot [209] use it to identify behind closed doors stories, which they associate to a high density (in addition to a structure devoid of any real center). Stiller et al [231,232] study the evolution of the density as a function of the number of characters in Shakespeare's plays.…”

Section: Graph Density and Edgementioning

confidence: 99%

See 4 more Smart Citations

Extraction and Analysis of Fictional Character Networks

Labatut

Bost

2019

ACM Comput. Surv.

View full text Add to dashboard Cite

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.

show abstract

Section: Vincent Labatut and Xavier Bostmentioning

confidence: 99%

Section: Attributementioning

confidence: 99%

Section: Co-occurrencesmentioning

confidence: 99%

Section: Vincent Labatut and Xavier Bostmentioning

confidence: 99%