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

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.

Artificial Intelligence and Mindfulness: Systematic Literature Review Based on Prisma Method

“…As a form of mindfulness, a multidisciplinary effort is required to determine how digital tools with AI function by examining their effects on various aspects of life. According to Acosta et al (2021), as future research directions for their studies, new questionnaires with questions tailored to the findings of this paper are currently being developed. It is anticipated that a significantly more significant number of individuals will participate in the new questionnaire to collect sufficient data for developing training models suitable for the application being studied, in this case, mindfulness.…”

“…Both intervention and control groups demonstrated significant reductions in depression and anxiety, but MYLO was not found to be more effective than ELIZA. Chatbots were found to be effective in reducing depression and anxiety, with retention rates ranging from 83% to 100%(Zhou et al, 2021) Acosta et al (2021). stated that artificial intelligence has been used to analyse a questionnaire on mindfulness therapy to develop future strategic plans.…”

Online Mindfulness Experience for Emotional Support to Healthcare staff in times of Covid-19

“…The tool used for the analysis was the Statistical Package for Social Sciences 24 (SPSS). The analysis of the open questions of this course have been published in another article [28].…”

Integrating Information Gain methods for Feature Selection in Distance Education Sentiment Analysis during Covid-19

“…In addition to decision tree, support vector machines (SVM) [21], [22], Naive Bayes [23], [24], [25], neural networks [26], [27], [28], [29], Bayesian networks [30], and maximum entropy [31], [32], [33] are often used in sentiment analysis. You can choose one of these methods based on the problem you want to study [4].…”