Paper Info
Title
Modeling affective character network for story analytics.
Abstract
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.
Year
DOI
Venue
2019
10.1016/j.future.2018.01.030
Future Generation Computer Systems
Keywords
Field
DocType
Story analytics,Affective relationship,Affective fluctuation,Affective event detection,Story-based recommender system
Social network,Comics,Computer science,Cognitive psychology,Narrative,Emotional Changes,Analytics,Affect (psychology),Distributed computing
Journal
Volume
ISSN
Citations 
92
0167-739X
2
PageRank 
References 
Authors
0.36
29
2
Name
Order
Citations
PageRank
O.-Joun Lee1367.98
Jason J. Jung21451135.51