García Faura, ÁlvaroHernández García, AlejandroFernández Martínez, FernandoDíaz de María, FernandoSan Segundo, Rubén2020-09-092020-09-092019-02-22Web Intelligence, (2019), 17(1), pp.: 29-40.2405-6456https://hdl.handle.net/10016/30807The electrodermal activity (EDA) is a psychophysiological indicator which can be considered a somatic marker of the emotional and attentional reaction of subjects towards stimuli. EDA measurements are not biased by the cognitive process of giving an opinion or a score to characterize the subjective perception, and group-level EDA recordings integrate the reaction of the whole audience, thus reducing the signal noise. This paper contributes to the field of affective video content analysis, extending previous novel work on the use of EDA as ground truth for prediction algorithms. Here, we label short video clips according to the audience's emotion (high vs. low) and attention (increasing vs. decreasing), derived from EDA records. Then, we propose a set of low-level audiovisual descriptors and train binary classifiers that predict the emotion and attention with 75% and 80% accuracy, respectively. These results, along with those of previous works, reinforce the usefulness of such low-level audiovisual descriptors to model video in terms of the induced affective response.11eng© 2019 IOS Press and the authors. All rights reserved.Electrodermal activityEmotionAttentionAffective video content analysisAudiovisual descriptorsresearch articleTelecomunicacioneshttps://doi.org/10.3233/WEB-19039829140Web Intelligence17AR/0000023302