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

Bibliographic Data

ID15514749
AuthorsMario Jojoa (0000-0002-6578-071X, Universidad de Deusto, corresponding author), Gema Castillo-Sánchez (0000-0002-8247-604X, Universidad de Valladolid), Begonya García-Zapirain (0000-0002-9356-1186, Universidad de Deusto), Isabel De La Torre Díez (0000-0003-3134-7720, Universidad de Valladolid), Manuel Franco (0000-0002-3639-2523, Hospital Virgen de la Concha)
Year2021
Volume18
Issue12
Pages6408-6408
Publication date2021-06-13
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueInternational Journal of Environmental Research and Public Health (JOURNAL)
Journal identifiersISSN: 1661-7827 • E-ISSN: 1660-4601
PublisherMultidisciplinary Digital Publishing Institute (PUBLISHER • CH)
DOI10.3390/ijerph18126408
PMID34199227
OpenAlexW3171854723
LanguageEN
Citations received2
References cited38

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

2019-20 coronavirus outbreak · Coronavirus disease 2019 (COVID-19 · Data science · Mindfulness · Natural language processing · Raw data · Sentiment analysis · Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2 · Clinical Psychology · Computer Science · COVID-19 and Mental Health · Medicine · Mental Health via Writing · Psychology · Sentiment Analysis and Opinion Mining · Internal Medicine · Virology