Exploring the prominence of<i>Romeo and Juliet</i>’s characters using weighted centrality measures

Masías, Víctor Hugo; Baldwin, Paula K.; Laengle, Sigifredo; Vargas, Augusto; Crespo, Fernando

doi:10.1093/llc/fqw029

Cited by 7 publications

(11 citation statements)

References 41 publications

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“…When the addressee is implicit, one uses the conversation turn assumption already leveraged for speaker identification. Certain authors define rules assuming that a speaker talks to the preceding speaker in the conversation [135,175], or the few preceding speakers [238], and/or to the following 22/75 Vincent Labatut and Xavier Bost one [163,173,195]. In the case of conversations involving more than two characters, certain authors assume that a speaker talks to everyone present [36,79,173], which can also be considered as co-occurrence with an additional constraint (not just being present, but also speaking).…”

Section: /75mentioning

confidence: 99%

“…However, most authors prefer to use simultaneously several such measures, as these are deemed complementary. They adopt various approaches: multiple thresholding [3,135,231], cluster analysis [163], supervised or semi-supervised classification [94,172,195], definition of a composite measure [144,244,244,246], PCA (Principal Components Analysis) completed by visual inspection [206,208]. In addition to purely structural features, certain authors dealing with alignment-related or work-specific roles use the narrative content: part-of-speech associated to each role [195], sentiment analysis applied to the dialogues [118] or co-occurrence context [94].…”

Section: Vincent Labatut and Xavier Bostmentioning

confidence: 99%

“…Vincent Labatut and Xavier Bost one [163,173,195]. In the case of conversations involving more than two characters, certain authors assume that a speaker talks to everyone present [36,79,173], which can also be considered as co-occurrence with an additional constraint (not just being present, but also speaking).…”

Section: /75mentioning

confidence: 99%

“…Main characters in two plays by Shakespeare: a) Troilus and Cressida[231]; b) Romeo and Juliet[163].…”

mentioning

confidence: 99%

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Extraction and Analysis of Fictional Character Networks

Labatut

Bost

2019

ACM Comput. Surv.

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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.

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Section: /75mentioning

confidence: 99%

Section: Vincent Labatut and Xavier Bostmentioning

confidence: 99%

Section: /75mentioning

confidence: 99%

“…Main characters in two plays by Shakespeare: a) Troilus and Cressida[231]; b) Romeo and Juliet[163].…”

mentioning

confidence: 99%

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Extraction and Analysis of Fictional Character Networks

Labatut

Bost

2019

ACM Comput. Surv.

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“…Even when the frequency or amount of character interaction is retained through tie weights (e.g. as in Masías et al 2017;Ruegg and Lee 2019), the temporal sequence of these interactions is lost. As narratologist Gérard Genette has argued, this elimination of time from the representation "is not only not sticking to the text, but is quite simply killing it" (Genette 1980, 35).…”

Section: Narrative As Dynamicmentioning

confidence: 99%

Measuring centrality in film narratives using dynamic character interaction networks

2020

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The tools of social network analysis offer a promising framework for studying fictional texts and the relational activity of the characters therein. The goal of this paper is to offer both a conceptual refinement of the project of measuring the centrality of characters within narratives using network tools, as well as the proposal of a novel measure with which to do so. Conceptually, we argue that as questions of time, order and sequence are central in narratives, measures of characters' narrative importance should be based on dynamic network representations which respect the time-ordering of narrative events. We suggest a directed dynamic measure of relative character importance based on character interactions and illustrate it through an examination of gender in the 2015 film Star Wars: The Force Awakens. We find that the measure helps illuminate important narrative dynamics which cannot be captured by static measures, and presents a platform on which future character network research can productively build.

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Digital detection of play characters’ relationships in Shakespeare’s plays: extended cross-correlation analysis of the character appearance frequencies

Yamada,

Murai,

Kumagai

2024

Int J Digit Libr

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We propose a method for visualizing literary works that quantitatively extracts the mutual relationships among play characters from the narrative of a storyline. The method first determines the cross-correlation of the appearance frequencies in the time domain between two play characters, which is calculated for all pairs of characters in each narrative. We also calculate the correlation among three play characters to find unique triangular relationships. Then we create a graphical representation of the relationships using node-link representations based on a physical potential model. The method is suitable for dramas, as demonstrated for ten famous Shakespeare plays. The resulting visualizations show good agreement with the conventional understanding of each play and also provide new insight into Shakespearean criticism.

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Exploring the prominence ofRomeo and Juliet’s characters using weighted centrality measures

Cited by 7 publications

References 41 publications

Extraction and Analysis of Fictional Character Networks

Extraction and Analysis of Fictional Character Networks

Measuring centrality in film narratives using dynamic character interaction networks

Digital detection of play characters’ relationships in Shakespeare’s plays: extended cross-correlation analysis of the character appearance frequencies

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