Sentiment Analysis Techniques Applied to Raw-Text Data from a Csq-8 Questionnaire about Mindfulness in Times of COVID-19 to Improve Strategy Generation

Mario Jojoa Acosta, Gema Castillo-Sánchez, Begonya Garcia-Zapirain, Isabel de la Torre Díez and Manuel Franco-Martín
Additional contact information
Mario Jojoa Acosta: Telecommunications Engineering, Engineering Faculty, University of Deusto, 48007 Bilbao, Spain
Gema Castillo-Sánchez: Department of Signal Theory, Communications, and Telematics Engineering, University of Valladolid, 47001 Valladolid, Spain
Begonya Garcia-Zapirain: Telecommunications Engineering, Engineering Faculty, University of Deusto, 48007 Bilbao, Spain
Isabel de la Torre Díez: Department of Signal Theory, Communications, and Telematics Engineering, University of Valladolid, 47001 Valladolid, Spain
Manuel Franco-Martín: Department of Psychiatry, Río Hortega University Hospital, 47012 Valladolid, Spain

IJERPH, 2021, vol. 18, issue 12, 1-21

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.

Keywords: mindfulness; stress; COVID-19; CSQ-8; natural language processing; deep learning; embedding; IMDB; swivel; neural networks (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1660-4601/18/12/6408/pdf (application/pdf)
https://www.mdpi.com/1660-4601/18/12/6408/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jijerp:v:18:y:2021:i:12:p:6408-:d:574325

Access Statistics for this article

IJERPH is currently edited by Ms. Jenna Liu

More articles in IJERPH from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().