EmoStory: Emotion Prediction and Mapping in Narrative Stories

Abstract

A well-designed story is built upon a sequence of plots and events. Each event has its purpose in piquing the audience's interest in the plot; thus, understanding the flow of emotions within the story is vital to its success. A story is usually built up through dramatic changes in emotion and mood to create resonance with the audience. The lack of research in this understudied field warrants exploring several aspects of the emotional analysis of stories. In this paper, we propose an encoder-decoder framework to perform sentence-level emotion recognition of narrative stories on both dimensional and categorical aspects, achieving MAE=0.0846 and 54% accuracy (8-class), respectively, on the EmoTales dataset and a reasonably good level of generalization to an untrained dataset. The first use of attention and multi-head attention mechanisms for emotion representation mapping (ERM) yields state-of-the-art performance in certain settings. We further present the preliminary idea of EmoStory, a concept that seamlessly predicts both dimensional and categorical space in an efficient manner, made possible with ERM. This methodology is useful in only one of the two aspects is available. In the future, these techniques could be extended to model the personality or emotional state of characters in stories, which could benefit the affective assessment of experiences and the creation of emotive avatars and virtual worlds