Emotion detection for social robots based on nlp transformers and an emotion ontology

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dc.contributor.authorGraterol, Wilfredo
dc.contributor.authorDiaz-Amado, Jose
dc.contributor.authorCardinale, Yudith
dc.contributor.authorDongo, Irvin
dc.contributor.authorLopes-Silva, Edmundo
dc.contributor.authorSantos-Libarino, Cleia
dc.date.accessioned2022-03-10T21:54:36Z
dc.date.available2022-03-10T21:54:36Z
dc.date.issued2021
dc.description.abstractFor social robots, knowledge regarding human emotional states is an essential part of adapting their behavior or associating emotions to other entities. Robots gather the information from which emotion detection is processed via different media, such as text, speech, images, or videos. The multimedia content is then properly processed to recognize emotions/sentiments, for example, by analyzing faces and postures in images/videos based on machine learning techniques or by converting speech into text to perform emotion detection with natural language processing (NLP) techniques. Keeping this information in semantic repositories offers a wide range of possibilities for implementing smart applications. We propose a framework to allow social robots to detect emotions and to store this information in a semantic repository, based on EMONTO (an EMotion ONTOlogy), and in the first figure or table caption. Please define if appropriate. an ontology to represent emotions. As a proof-of-concept, we develop a first version of this framework focused on emotion detection in text, which can be obtained directly as text or by converting speech to text. We tested the implementation with a case study of tour-guide robots for museums that rely on a speech-to-text converter based on the Google Application Programming Interface (API) and a Python library, a neural network to label the emotions in texts based on NLP transformers, and EMONTO integrated with an ontology for museums; thus, it is possible to register the emotions that artworks produce in visitors. We evaluate the classification model, obtaining equivalent results compared with a state-of-the-art transformer-based model and with a clear roadmap for improvement.es_PE
dc.description.uriTrabajo académicoes_PE
dc.identifier.doi10.3390/s21041322es_PE
dc.identifier.issn14248220
dc.identifier.urihttps://hdl.handle.net/20.500.12590/17053
dc.language.isoenges_PE
dc.publisherMDPI AGes_PE
dc.publisher.countryPEes_PE
dc.relationinfo:eu-repo/semantics/articlees_PE
dc.relation.urihttps://www.scopus.com/record/display.uri?eid=2-s2.0-85100779547&origin=resultslist&sort=plf-f&src=s&nlo=&nlr=&nls=&sid=dd875f57cfc47807be0ca661a187cd3a&sot=aff&sdt=cl&cluster=scopubyr%2c%222021%22%2ct&sl=48&s=AF-ID%28%22Universidad+Cat%c3%b3lica+San+Pablo%22+60105300%29&relpos=28&citeCnt=5&searchTerm=&featureToggles=FEATURE_NEW_DOC_DETAILS_EXPORT:1es_PE
dc.rightsinfo:eu-repo/semantics/restrictedAccesses_PE
dc.sourceUniversidad Católica San Pabloes_PE
dc.sourceRepositorio Institucional - UCSPes_PE
dc.subjectEmotion detectiones_PE
dc.subjectNatural language processinges_PE
dc.subjectOntologyes_PE
dc.subjectSocial robotses_PE
dc.subjectText classificationes_PE
dc.subject.ocdehttps://purl.org/pe-repo/ocde/ford#2.02.03es_PE
dc.titleEmotion detection for social robots based on nlp transformers and an emotion ontologyes_PE
dc.typeinfo:eu-repo/semantics/article
dc.type.versioninfo:eu-repo/semantics/publishedVersiones_PE
renati.typehttps://purl.org/pe-repo/renati/type#trabajoAcademico
thesis.degree.disciplineIngeniería Electrónica y de Telecomunicacioneses_PE
thesis.degree.grantorUniversidad Católica San Pablo. Departamento de Ingeniería Eléctrica y Electrónicaes_PE
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