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Modeling affective character network for story analytics

机译：为故事分析建模情感角色网络

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Consideration of the stories included in the narrative works is important for analyzing and providing narrative works (e.g., movies, novels, and comics) to users. In this study, we analyzed the stories in a narrative work with three goals: (i) eliciting, (ii) modeling, and (iii) utilizing the stories. Based upon our previous studies regarding ‘character networks’ (i.e., social networks among characters in the stories), we elicited the stories with three methods: (i) composing affective character networks with affective relationships among the characters, (ii) measuring temporal changes in tension according to the flows of the stories, and (iii) detecting affective events which refer to dramatic changes in the tension. The affective relationships contain emotional changes of the characters on each segment of the stories. By aggregating the characters’ emotional changes, we measured the tension of each segment. We called it ‘Affective Fluctuation’ and represented it as a discrete function (Affective Fluctuation Function, AFF). The AFFs enable us to detect affective events by using gradients of them and measure similarities among the stories by comparing their shapes. Also, we proposed a computational model of the stories by annotating the affective events and characters involved in those events. Finally, we demonstrated a practical application with a recommendation method which exploited the similarities between stories. Additionally, we verified the reliabilities and efficiencies of the proposed method for narrative works in the real world.
机译：考虑叙事作品中包含的故事对于分析叙事作品（例如电影，小说和漫画）并向用户提供非常重要。在这项研究中，我们以三个目标分析了叙事作品中的故事：（i）引出，（ii）建模和（iii）利用故事。根据我们先前对“角色网络”（即故事中人物之间的社交网络）的研究，我们通过三种方法得出了故事：（i）构成具有人物之间情感关系的情感人物网络，（ii）测量时间变化根据故事的流向而处于紧张状态；（iii）检测涉及紧张状态急剧变化的情感事件。情感关系包含故事每个片段中人物的情感变化。通过汇总角色的情感变化，我们测量了每个部分的紧张程度。我们将其称为“情感波动”，并将其表示为离散函数（情感波动函数，AFF）。 AFF使我们能够通过使用情感梯度来检测情感事件，并通过比较它们的形状来度量故事之间的相似性。此外，我们通过注释情感事件和涉及这些事件的角色，提出了故事的计算模型。最后，我们利用推荐方法演示了一个实际应用，该推荐方法利用了故事之间的相似性。此外，我们验证了所提出的方法在现实世界中的可靠性和效率。

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