Emotion Detection for Social Robots Based on NLP Transformers and an Emotion Ontology
| Autor: | Yudith Cardinale, Wilfredo Graterol, Cleia Santos-Libarino, Irvin Dongo, Edmundo Lopes-Silva, Jose Diaz-Amado |
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| Přispěvatelé: | Universidad Simon Bolivar (USB), Escola Politécnica da Universidade Federal da Bahia (UFBA), Universidade Federal da Bahia (UFBA), Universidad Católica San Pablo (UCSP), ESTIA Recherche, Ecole Supérieure des Technologies Industrielles Avancées (ESTIA) |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
0209 industrial biotechnology
text classification Computer science Emotions emotion detection 02 engineering and technology Ontology (information science) Social robots social robots computer.software_genre lcsh:Chemical technology Biochemistry Article Analytical Chemistry 020901 industrial engineering & automation 0202 electrical engineering electronic engineering information engineering Humans Speech [INFO]Computer Science [cs] lcsh:TP1-1185 ontology Electrical and Electronic Engineering natural language processing Instrumentation Transformer (machine learning model) Social robot Emotion detection Artificial neural network purl.org/pe-repo/ocde/ford#2.02.02 [http] business.industry Ontology Natural language processing Robotics Atomic and Molecular Physics and Optics Semantics Text classification Robot Table (database) 020201 artificial intelligence & image processing Artificial intelligence business computer |
| Zdroj: | Sensors, Vol 21, Iss 1322, p 1322 (2021) CONCYTEC-Institucional Consejo Nacional de Ciencia Tecnología e Innovación Tecnológica instacron:CONCYTEC Sensors (Basel, Switzerland) Sensors Sensors, MDPI, 2021, 21 (4), pp.1322. ⟨10.3390/s21041322⟩ Volume 21 Issue 4 |
| ISSN: | 1424-8220 |
| DOI: | 10.3390/s21041322⟩ |
| Popis: | For 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. |
| Databáze: | OpenAIRE |
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