Identifying turning points in animated cartoons

Liu, Chang; Shmilovici, Armin

doi:10.1016/j.eswa.2019.01.003

Cited by 10 publications

(8 citation statements)

References 8 publications

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“…This study applies simple statistical features, which are already validated by the existing studies in the computational narrative analysis. We measure inner changes of characters by using (i) average lengths of dialogues [28,41,51] and (ii) ratios of out-degree for in-degree [47]. Although these features are simple and intuitive, they have exhibited reliable performance for analyzing flows of stories in the existing studies.…”

Section: Revealing Personality Changes Of Charactersmentioning

confidence: 99%

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

2021

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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.

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Section: Revealing Personality Changes Of Charactersmentioning

confidence: 99%

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

2021

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“…By finding the clock drift (maximum difference) between the two clocks, the critical event within the play is detected. In our previous work, we adapted the two-clocks theory to the domain of video analytics, and tested it on the first season of the Flintstones cartoon series to identify turning points within the stories [19] using only the subtitles. In this work, however, we utilize the visual information, rather than text, to implement the two-clocks theory and craft story-related features.…”

Section: Story-related Featuresmentioning

confidence: 99%

“…In our previous work, we extended Lotker's two-clock theory [18] for detecting story turning points in The Flintstones' subtitle text and achieved promising results on both turning points and key story element detection [19]. However, the ability of distinguishing key story elements is still lacking.…”

Section: Related Workmentioning

confidence: 99%

Towards story-based classification of movie scenes

Liu

Shmilovici

2020

PLoS ONE

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Humans are entertained and emotionally captivated by a good story. Artworks, such as operas, theatre plays, movies, TV series, cartoons, etc., contain implicit stories, which are conveyed visually (e.g., through scenes) and audially (e.g., via music and speech). Story theorists have explored the structure of various artworks and identified forms and paradigms that are common to most well-written stories. Further, typical story structures have been formalized in different ways and used by professional screenwriters as guidelines. Currently, computers cannot yet identify such a latent narrative structure of a movie story. Therefore, in this work, we raise the novel challenge of understanding and formulating the movie story structure and introduce the first ever story-based labeled dataset-the Flintstones Scene Dataset (FSD). The dataset consists of 1, 569 scenes taken from a manual annotation of 60 episodes of a famous cartoon series, The Flintstones, by 105 distinct annotators. The various labels assigned to each scene by different annotators are summarized by a probability vector over 10 possible story elements representing the function of each scene in the advancement of the story, such as the Climax of Act One or the Midpoint. These elements are learned from guidelines for professional script-writing. The annotated dataset is used to investigate the effectiveness of various story-related features and multi-label classification algorithms for the task of predicting the probability distribution of scene labels. We use cosine similarity and KL divergence to measure the quality of predicted distributions. The best approaches demonstrated 0.81 average similarity and 0.67 KL divergence between the predicted label vectors and the ground truth vectors based on the manual annotations. These results demonstrate the ability of machine learning approaches to detect the narrative structure in movies, which could lead to the development of story-related video analytics tools, such as automatic video summarization and recommendation systems.

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“…The existing studies on the computational narrative analysis mostly focused on conventional narrative multimedia, such as movies, novels, and TV series [27][28][29]. Also, these studies aimed at analyzing storytelling methods rather than subjects and backgrounds; e.g., plot structures [29][30][31], character roles [32,33], major events [34][35][36], and so on. Although a few studies [37,38] attempted to classify narrative works into genres, they were also based on interactions among characters, which are related to narrative development rather than subjects.…”

Section: Related Workmentioning

confidence: 99%

“…A few studies applied sentiment analysis to enrich the social networks [34,53]. Node centrality on the networks reflected character roles [33], structures of the social networks were correlated to major events in stories [34][35][36], changes in the structures had relevancy with fluency of stories [54], and occurrence frequencies of characters showed plot structures [30,31]. These extracted narrative features were applied to mainly summarization [50,55,56].…”

Section: Related Workmentioning

confidence: 99%

Discovering Social Desires and Conflicts from Subculture Narrative Multimedia

et al. 2020

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This study aims at discovering social desires and conflicts from subculture narrative multimedia. Since one of the primary purposes in the subculture consumption is vicarious satisfaction, the subculture works straightforwardly describe what their readers want to achieve and break down. The latent desires and conflicts are useful for understanding our society and realizing smart governance. To discover the social issues, we concentrate on that each subculture genre has a unique imaginary world that consists of inventive subjects. We suppose that the subjects correspond to individual social issues. For example, game fiction, one of the popular genres, describes a world like video games. Under game systems, everyone gets the same results for the same efforts, and it can be interpreted as critics for the social inequality issue. Therefore, we first extract subjects of genres and measure the membership degrees of subculture works for each genre. Using the subjects and membership degrees, we build a genealogy tree of subculture genres by tracing their evolution and differentiation. Then, we extract social issues by searching for the subjects that come from the real world, not imaginary. If a subculture work criticizes authoritarianism, it might include subjects such as government officials and bureaucrats. A combination of the social issues and genre genealogy tree will show diachronic changes in our society. We have evaluated the proposed methods by extracting social issues reflected in Korean web novels.

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Identifying turning points in animated cartoons

Cited by 10 publications

References 8 publications

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

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

Towards story-based classification of movie scenes

Discovering Social Desires and Conflicts from Subculture Narrative Multimedia

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