RT journal article
T1 Improving the Fitness Function of an Evolutionary Suspense Generator Through Sentiment Analysis
A1 Torre Moreno, Pablo de la
A1 León, Carlos
A1 Salguero Hidalgo, Alberto Gabriel
A2 Ingeniería Informática
K1 Sentiment analysis
K1 Predictive models
K1 Evolutionary computation
K1 Computational modeling
K1 Prediction algorithms
K1 Distance measurement
K1 Correlation
K1 Automatic story generation
K1 genetic programming
K1 predictive model
K1 sentiment analysis
K1 suspense
AB 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.
PB IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
SN 2169-3536
YR 2021
FD 2021
LK http://hdl.handle.net/10498/24767
UL http://hdl.handle.net/10498/24767
LA eng
DS Repositorio Institucional de la Universidad de Cádiz
RD 13-nov-2026