Emotion-based character clustering for managing story-based contents: a cinemetric analysis

Jung, Jason J.; You, Eun-Soon; Park, Seung-Bo

doi:10.1007/s11042-012-1133-x

Cited by 23 publications

(19 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…See [133] for a recent review of the field. Jung et al note that current face detection methods are efficient mainly on front views of the faces [118]: this is a strong limitation in our context, as a character can be filmed under a variety of angles. Weng et al also observe that current automatic methods do not reach satisfying enough performances, which is why they first proceed manually [267].…”

Section: Visual Narrativesmentioning

confidence: 99%

“…When the script is properly formatted and structured, it is relatively straightforward to extract this information automatically. Authors have proposed methods based on exact string matching [63,118,173], regular expression [238,272], or a custom parser [200]. a) b) c) Fig.…”

Section: Semi-structured Textmentioning

confidence: 99%

“…A few multimodal methods able to perform speaker segmentation using both audio and video have been proposed, but we describe them later as they all additionally solve speaker clustering (and therefore speaker diarization). Scripts can be used to distinguish speakers or on-screen characters as in [118,190,191]. A script is not time-stamped, so this approach requires first solving an additional problem called script alignment, which consists in determining the exact time at which each line contained in the script occurs in the video.…”

Section: /75mentioning

confidence: 99%

“…For instance, based on the assumption that the title of the fiction work is known, Tran et al [245] retrieve its list of characters from IMDb, look for their picture using Google Image, and leverage this information to infer character names in the movie through matching. In the context of fiction works though, the favored approach is to leverage scripts [118,148,190,282]. This again raises the issue of script alignment (with the corresponding video, transcript, or subtitle), as scripts are not time-stamped.…”

Section: /75mentioning

confidence: 99%

See 3 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: Visual Narrativesmentioning

confidence: 99%

Section: Semi-structured Textmentioning

confidence: 99%

Section: /75mentioning

confidence: 99%

Section: /75mentioning

confidence: 99%

See 2 more Smart Citations

Extraction and Analysis of Fictional Character Networks

Labatut

Bost

2019

ACM Comput. Surv.

View full text Add to dashboard Cite

show abstract

“…A lot of studies have reported extraction of semantically meaningful information from movie data [1][2][3][4]. Such information is useful in browsing, searching, and genre classification.…”

Section: Introductionmentioning

confidence: 99%

Scene Boundary Detection from Movie Dialogue: A Genetic Algorithm Approach

Kundu¹,

Das²,

Bandyopadhyay³

2013

Polibits

View full text Add to dashboard Cite

Abstract-Movie scripts are a rich textual resource that can be tapped for movie content analysis. This article describes a mechanism for fragmenting a sequence of movie script dialogue into scene-wise groups. In other words, it attempts to locate scene transitions using information acquired from a sequence of dialogue units. We collect movie scripts from a web archive. Thereafter, we preprocess them to develop a resource of dialogues. We feed the dialogue sequence from a script to a Genetic Algorithm (GA) framework. The system fragments the sequence into adjacent groups of dialogue units or output 'scenes'. We use SentiWordnet scores and Wordnet distance for dialogue units to optimize this grouping so that adjacent scenes are semantically most dissimilar. Then we compare the resulting fragmented dialogue sequence with the original scene-wise alignment of dialogue in the script.

show abstract