Automatic Identification of Emotional Information in Spanish TV Debates and Human–Machine Interactions

Abstract

Automatic emotion detection is a very attractive field of research that can help build more natural human–machine interaction systems. However, several issues arise when real scenarios are considered, such as the tendency toward neutrality, which makes it difficult to obtain balanced datasets, or the lack of standards for the annotation of emotional categories. Moreover, the intrinsic subjectivity of emotional information increases the difficulty of obtaining valuable data to train machine learning-based algorithms. In this work, two different real scenarios were tackled: human–human interactions in TV debates and human–machine interactions with a virtual agent. For comparison purposes, an analysis of the emotional information was conducted in both. Thus, a profiling of the speakers associated with each task was carried out. Furthermore, different classification experiments show that deep learning approaches can be useful for detecting speakers’ emotional information, mainly for arousal, valence, and dominance levels, reaching a 0.7F1-score.