Improving the Fitness Function of an Evolutionary Suspense Generator Through Sentiment Analysis

Abstract

The perception of suspense in stories is affected not only by general literary aspects like narrative structure and linguistic features, but also by anticipation and evocation of feelings like aversion, disgust or empathy. As such, it is possible to alter the feeling of suspense by modifying components of a story that convey these feelings to the audience. Based on a previous straightforward model of suspense adaptation, this paper describes the design, implementation and evaluation of a computational system that adapts narrative scenes for conveying a specific user-defined amount of suspense. The system is designed to address the impact of different types of emotional components on the reader. The relative weighted suspense of these components is computed with a regression model based on a sentiment analysis tool, and used as a fitness function in an evolutionary algorithm. This new function is able to identify the different weights on the prediction of suspense in aspects like outcome, decorative elements, or threat's appearance. The results indicate that this approach represents a significant improvement over the previous existing approach.

Effects of negative emotional valence on readers’ text processing and memory for text: an eye-tracking study

“…A score between − 0.05 and + 0.22 is considered neutral. The tool has been used in several studies (e.g., Delatorre et al, 2021;Zucco et al, 2020) and its ecological validity has also been proved. For instance, Delatorre et al (2020) found that suspense book passages ranged from negative to neutral-negative Semantria scores (M = 0.189, SD = 0.134), while non-suspense passages ranged from neutral to positive.…”