RT info:eu-repo/semantics/article
T1 Sentiment analysis techniques applied to raw-text data from a Csq-8 questionnaire about mindfulness in times of COVID-19 to Improve strategy generation
A1 Jojoa Acosta, Mario
A1 Castillo Sánchez, Gema Anabel
A1 Garcia Zapirain, Begonya
A1 Torre Díez, Isabel de la
A1 Franco Martín, Manuel Ángel
K1 Mindfulness
K1 Meditación
K1 Stress
K1 Estrés
K1 COVID-19
K1 Natural Language Processing (NLP)
K1 Procesamiento en lenguaje natural (Informática)
K1 Deep learning (Machine learning)
K1 Neural networks (Computer science)
K1 Redes neuronales (Informática)
K1 33 Ciencias Tecnológicas
K1 32 Ciencias Médicas
AB 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.
PB MDPI
SN 1660-4601
YR 2021
FD 2021
LK https://uvadoc.uva.es/handle/10324/59811
UL https://uvadoc.uva.es/handle/10324/59811
LA eng
NO International Journal of Environmental Research and Public Health, 2021, Vol. 18, Nº. 12, 6408
NO Producción Científica
DS UVaDOC
RD 01-jun-2024