Modeling affective character network for story analytics

Lee, O-Joun; Jung, Jason J.

doi:10.1016/j.future.2018.01.030

Cited by 20 publications

(32 citation statements)

References 31 publications

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“…Furthermore, hierarchical structures of stories are a part of common sense, as well as their time-sequential features [20,21,[40][41][42]. The computational narrative analysis is not only for user applications [7,8] but also for analysis itself for supporting literature or narratology studies [43,44]. For this purpose, we have to look inside stories, not treating the stories monolithic.…”

Section: Accuracy Of Story2vec Models For Measuring Story Similaritymentioning

confidence: 99%

“…Various studies [1][2][3][4] have been conducted for character networks (i.e., social networks between characters that appear in stories) to analyze stories in narrative multimedia (i.e., creative works that contain stories and are distributed through multimedia) automatically. They applied the analysis results on various applications-summarizing [5,6], recommending [7,8], indexing [9,10], and even generating [11] narrative multimedia. However, the character network model also has limitations.…”

Section: Introductionmentioning

confidence: 99%

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Learning Hierarchical Representations of Stories by Using Multi-Layered Structures in Narrative Multimedia

Lee

Jung

Kim

2020

Sensors

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Narrative works (e.g., novels and movies) consist of various utterances (e.g., scenes and episodes) with multi-layered structures. However, the existing studies aimed to embed only stories in a narrative work. By covering other granularity levels, we can easily compare narrative utterances that are coarser (e.g., movie series) or finer (e.g., scenes) than a narrative work. We apply the multi-layered structures on learning hierarchical representations of the narrative utterances. To represent coarser utterances, we consider adjacency and appearance of finer utterances in the coarser ones. For the movies, we suppose a four-layered structure (character roles ∈ characters ∈ scenes ∈ movies) and propose three learning methods bridging the layers: Char2Vec, Scene2Vec, and Hierarchical Story2Vec. Char2Vec represents a character by using dynamic changes in the character’s roles. To find the character roles, we use substructures of character networks (i.e., dynamic social networks of characters). A scene describes an event. Interactions between characters in the scene are designed to describe the event. Scene2Vec learns representations of a scene from interactions between characters in the scene. A story is a series of events. Meanings of the story are affected by order of the events as well as their content. Hierarchical Story2Vec uses sequential order of scenes to represent stories. The proposed model has been evaluated by estimating the similarity between narrative utterances in real movies.

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Section: Accuracy Of Story2vec Models For Measuring Story Similaritymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Learning Hierarchical Representations of Stories by Using Multi-Layered Structures in Narrative Multimedia

Lee

Jung

Kim

2020

Sensors

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“…To construct a character network it is necessary to correctly identify the character appearance events in the videos. Ideally, computer vision based approaches could be used for this task; however, such tools (e.g., Microsoft Video-Indexer) for detecting character faces are usually trained on real-life videos rather than annotated cartoons, and hence they are not effective in cartoon movies [8]. Therefore, for the construction of the character networks, we made use of the dataset of Gupta et al, which consists of 25,184 densely annotated 3-second video clips taken from the Flintstones cartoons.…”

Section: Story-related Features: the Character Networkmentioning

confidence: 99%

“…Tran et al proposed the idea of building a co-occurrence character network [20], which has been extended to an affective character network, representing the emotional relationship among characters. Based on this, Lee and Jung proposed a graph-style story model which reflects the story development [8]. Most recently, Lee and Jung proposed a character network embedding based method to detect subplots within a story.…”

Section: Related Workmentioning

confidence: 99%

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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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Data‐driven exploratory approach on player valuation in football transfer market

Kim

Bui

Jung

2019

Concurrency and Computation

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Summary Transfer markets in football have attracted the interest of researchers in economy and management. In this paper, we propose a high level analysis approach for classifying player valuation based on their performance during recent seasons. In particular, several data analysis techniques such as regression analysis, feature selection, and cluster analysis are presented for classifying players in term of performances and transfer fee. Specifically, by collecting and analyzing data from Wholescored, the largest detailed football statistics website, we have defined players into four groups, which include (1) Low performance and low transfer fee (LPLF), (2) Low performance and high transfer fee (LPHF), (3) high performance and high transfer fee (HPHF), and (4) high performance and low transfer fee (HPLF). The results in the implementation section show that, with the differences positions, there are different required skills that affect to the performance of players. We expect that this study can contribute to the management of Football Teams in terms of integrating these analyses into their management strategy.

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Modeling affective character network for story analytics

Cited by 20 publications

References 31 publications

Learning Hierarchical Representations of Stories by Using Multi-Layered Structures in Narrative Multimedia

Learning Hierarchical Representations of Stories by Using Multi-Layered Structures in Narrative Multimedia

Towards story-based classification of movie scenes

Data‐driven exploratory approach on player valuation in football transfer market

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