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: | Begonya Garcia-Zapirain, Isabel de la Torre Díez, Manuel A. Franco-Martín, Mario Fernando Jojoa Acosta, Gema Castillo-Sánchez |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Mindfulness
mindfulness Computer science Deep learning (Machine learning) Health Toxicology and Mutagenesis computer.software_genre stress 0302 clinical medicine Surveys and Questionnaires Stress (linguistics) automotive_engineering IMDB Artificial neural network CSQ-8 neural networks Redes neuronales (Informática) Objective approach Estrés Medicine Transfer of learning Natural language processing Coronavirus disease 2019 (COVID-19) Natural Language Processing (NLP) Work related Meditación Article Neural networks (Computer science) 03 medical and health sciences embedding Text mining swivel 32 Ciencias Médicas Artificial Intelligence Humans natural language processing Procesamiento en lenguaje natural (Informática) business.industry SARS-CoV-2 Deep learning Sentiment analysis Public Health Environmental and Occupational Health COVID-19 deep learning 030227 psychiatry Word lists by frequency Quality of Life Artificial intelligence business computer 030217 neurology & neurosurgery 33 Ciencias Tecnológicas |
| Zdroj: | International Journal of Environmental Research and Public Health Volume 18 Issue 12 International Journal of Environmental Research and Public Health, Vol 18, Iss 6408, p 6408 (2021) |
| ISSN: | 1660-4601 |
| Popis: | Producción Científica 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. Junta de Castilla y León, Gerencia Regional de Salud - (grant GRS COVID 90/A/20) |
| Databáze: | OpenAIRE |
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