Sentiment analysis techniques applied to raw-text data from a csq-8 questionnaire about mindfulness in times of Covid-19 to improve strategy generation

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dc.contributor.authorJojoa Acosta, Mario Fernando
dc.contributor.authorCastillo Sánchez, Gema
dc.contributor.authorGarcía-Zapirain, Begoña
dc.contributor.authorTorre Díez, Isabel de la
dc.contributor.authorFranco Martín, Manuel Ángel
dc.date.accessioned2025-05-08T07:21:14Z
dc.date.available2025-05-08T07:21:14Z
dc.date.issued2021-06-13
dc.date.updated2025-05-08T07:21:14Z
dc.description.abstractThe 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 stagesen
dc.description.sponsorshipThis project has been supported by Gerencia Regional de Salud (Castilla y Leon Health Service) through a grant GRS COVID 90/A/20en
dc.identifier.citationAcosta, M. J., Castillo-Sánchez, G., Garcia-Zapirain, B., de la Torre Díez, I., & Franco-Martín, M. (2021). Sentiment analysis techniques applied to raw-text data from a csq-8 questionnaire about mindfulness in times of Covid-19 to improve strategy generation. International Journal of Environmental Research and Public Health, 18(12). https://doi.org/10.3390/IJERPH18126408
dc.identifier.doi10.3390/IJERPH18126408
dc.identifier.eissn1660-4601
dc.identifier.issn1661-7827
dc.identifier.urihttp://hdl.handle.net/20.500.14454/2687
dc.language.isoeng
dc.publisherMDPI AG
dc.rights© 2021 by the authors
dc.subject.otherMindfulness
dc.subject.otherStress
dc.subject.otherCOVID-19
dc.subject.otherCSQ-8
dc.subject.otherNatural language processing
dc.subject.otherDeep learning
dc.subject.otherEmbedding
dc.subject.otherIMDB
dc.subject.otherSwivel
dc.subject.otherNeural networks
dc.titleSentiment analysis techniques applied to raw-text data from a csq-8 questionnaire about mindfulness in times of Covid-19 to improve strategy generationen
dc.typejournal article
dcterms.accessRightsopen access
oaire.citation.issue12
oaire.citation.titleInternational Journal of Environmental Research and Public Health
oaire.citation.volume18
oaire.licenseConditionhttps://creativecommons.org/licenses/by/4.0/
oaire.versionVoR
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