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Título
Sentiment analysis techniques applied to raw-text data from a Csq-8 questionnaire about mindfulness in times of COVID-19 to Improve strategy generation
Autor
Año del Documento
2021
Editorial
MDPI
Descripción
Producción Científica
Documento Fuente
International Journal of Environmental Research and Public Health, 2021, Vol. 18, Nº. 12, 6408
Abstract
The use of artificial intelligence in health care has grown quickly. In this sense, we present our work related to the application of Natural Language Processing techniques, as a tool to analyze the sentiment perception of users who answered two questions from the CSQ-8 questionnaires with raw Spanish free-text. Their responses are related to mindfulness, which is a novel technique used to control stress and anxiety caused by different factors in daily life. As such, we proposed an online course where this method was applied in order to improve the quality of life of health care professionals in COVID 19 pandemic times. We also carried out an evaluation of the satisfaction level of the participants involved, with a view to establishing strategies to improve future experiences. To automatically perform this task, we used Natural Language Processing (NLP) models such as swivel embedding, neural networks, and transfer learning, so as to classify the inputs into the following three categories: negative, neutral, and positive. Due to the limited amount of data available—86 registers for the first and 68 for the second—transfer learning techniques were required. The length of the text had no limit from the user’s standpoint, and our approach attained a maximum accuracy of 93.02% and 90.53%, respectively, based on ground truth labeled by three experts. Finally, we proposed a complementary analysis, using computer graphic text representation based on word frequency, to help researchers identify relevant information about the opinions with an objective approach to sentiment. The main conclusion drawn from this work is that the application of NLP techniques in small amounts of data using transfer learning is able to obtain enough accuracy in sentiment analysis and text classification stages.
Materias (normalizadas)
Mindfulness
Meditación
Stress
Estrés
COVID-19
Natural Language Processing (NLP)
Procesamiento en lenguaje natural (Informática)
Deep learning (Machine learning)
Neural networks (Computer science)
Redes neuronales (Informática)
Materias Unesco
33 Ciencias Tecnológicas
32 Ciencias Médicas
ISSN
1660-4601
Revisión por pares
SI
Patrocinador
Junta de Castilla y León, Gerencia Regional de Salud - (grant GRS COVID 90/A/20)
Version del Editor
Propietario de los Derechos
© 2021 The authors
Idioma
eng
Tipo de versión
info:eu-repo/semantics/publishedVersion
Derechos
openAccess
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