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Project CAVIAR CApturing VIewers' Affective Response

  • In this project we propose the automatic analysis of the relation between the audiovisual characteristics of a multimedia production and the impact caused in its audience. With this aim, potential synergies are explored between different areas of knowledge including, among others: audiovisual communication, computer vision, multimodal systems, biometric sensors, social network analysis, opinion mining, and affective computing. Our efforts are oriented towards combining these technologies to introduce novel computational models that could predict the reactions of spectators to multimedia elements across different media and moments. On the one hand, we study the cognitive and emotional response of the spectators while they are watching the media instances, using neuroscience techniques and biometric sensors. On the other hand, we also study the reaction shown by the audience on social networks by relying on the automatic collection and analysis of different metadata related to the mediaIn this project we propose the automatic analysis of the relation between the audiovisual characteristics of a multimedia production and the impact caused in its audience. With this aim, potential synergies are explored between different areas of knowledge including, among others: audiovisual communication, computer vision, multimodal systems, biometric sensors, social network analysis, opinion mining, and affective computing. Our efforts are oriented towards combining these technologies to introduce novel computational models that could predict the reactions of spectators to multimedia elements across different media and moments. On the one hand, we study the cognitive and emotional response of the spectators while they are watching the media instances, using neuroscience techniques and biometric sensors. On the other hand, we also study the reaction shown by the audience on social networks by relying on the automatic collection and analysis of different metadata related to the media elements, such as popularity, sharing patterns, ratings and commentaries.show moreshow less

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Metadaten
Author:Fernando Fernández-Martínez, Zoraida Callejas, Ricardo Kleinlein, Cristina Luna JiménezORCiDGND, Juan Manuel Montero, José Manuel Pardo
URN:urn:nbn:de:bvb:384-opus4-1226860
Frontdoor URLhttps://opus.bibliothek.uni-augsburg.de/opus4/122686
URL:http://journal.sepln.org/sepln/ojs/ojs/index.php/pln/article/view/6108
ISSN:1135-5948OPAC
Parent Title (Spanish):Procesamiento del Lenguaje Natural
Publisher:Sociedad Española para el Procesamiento del Lenguaje Natural
Type:Article
Language:English
Date of Publication (online):2025/06/04
Year of first Publication:2019
Publishing Institution:Universität Augsburg
Release Date:2025/06/05
Issue:63
First Page:155
Last Page:158
Institutes:Fakultät für Angewandte Informatik
Fakultät für Angewandte Informatik / Institut für Informatik
Fakultät für Angewandte Informatik / Institut für Informatik / Lehrstuhl für Menschzentrierte Künstliche Intelligenz
Dewey Decimal Classification:0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik
Licence (German):CC-BY-NC-ND 4.0: Creative Commons: Namensnennung - Nicht kommerziell - Keine Bearbeitung (mit Print on Demand)