## 1 Introduction
\begin{table}
\begin{tabular}{p{108.4 pt} p{142.3 pt} p{142.3 pt} p{142.3 pt}} \hline
**Proposal** & **Techniques** & **Results** & **Reference** \\ \hline Sentiment analysis of sentences obtained from tweets. & TextBlob, Word Cloud. & The study of 2 datasets observes the impact of social networks that generate insecurity and uncertainty regarding COVID-19. & [PERSON] et al. [8] \\ Understanding the role of sentiment analysis and opinion mining in COVID-19. & Paragraph vector (Doc2 Vec), word cloud, and Gaussian fuzzy logic. & A systematic study to understand statistics and keywords about COVID-19. & [PERSON] et al. [9] \\ Using NLP to identify aspects causing the most stress, anxiety, and trauma in Indian citizens by COVID-19. & Machine learning, TextBlob. & General public’s attitude towards stress. Anxiety also had no change in the last part of the study. & [PERSON] et al. [2] \\ To explore the psychology of patients with COVID-19 during hospitalization. & Phenomenological technique with semistructured interviews. & To develop an attitude to face the disease despite fear and uncertainty. & [PERSON] et al. [10] \\ Identifying key areas of loneliness in diverse communities during the pandemic through data mining on the social network Twitter. & Machine learning (theme modeling), hierarchical modeling, topic modeling and NLP. & Addresses novel problems such as loneliness during COVID-19 and the potential nullity of evolving mental-health problems. & [PERSON] and [PERSON] [11] \\ Explored the dynamics and flow of behavioral changes among Twitter users during the pandemic. & NLP and social-network analysis techniques. & The percentage of tweets with negative sentiment in all intervals exceeded that of positive and neutral sentiments. Furthermore, while negative tweets had a continuous decline, neutral and positive tweets followed a fluctuating trend. & [PERSON] et al. [12] \\ Identified emotions that dominated citizens in the pandemic from millions of tweets in Portuguese (Brazil) and English (US), and sentiment analysis. & NLP, NLP, NLP, WordNetLemmatizer, classifier of logistic regression, word cloud, and py1 DAwis tool, by confinement. & The study demonstrated the economic impact, policy, treatments, and reports on anxiety caused by confinement. & [PERSON] and [PERSON] [13] \\ Analyzing tweets between 25 and 28 March to measure feelings about confinement. & R software and word cloud. & The study showed that, despite negativity, fear, disgust, and sadness about the lock-in, the prominent sentiment was positive, followed by confidence and commitment from citizens to lower the spike in contagions. & [PERSON] et al. [14] \\ Explored and compared the impact of the pandemic (perceived stress, risk, and fear of being infected, estimated severity, compliance with containment, quality of life, quality of relationships, loneliness, resilience) on a sample of French and Reunion Island, the most populous overseas department, citizens. & Results reflect significant differences between the two samples. Comparisons indicate that the metropolitan sample had significantly higher perceived risk, confinement compliance, and loneliness scores. & Results reflect significant differences between the two samples. Comparisons indicate that the metropolitan sample had significantly higher perceived risk, confinement compliance, and loneliness scores. & [PERSON] et al. [15] \\ \hline \end{tabular}
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Table 1: Representative publications that addressed the COVID-19 issue from a computational model. The remainder of the paper is structured as follows: Section 2 presents the developed methodology, which was based on an exploratory study described in two phases. Section 3 analyzes and compares the proposed processes. Section 4 presents the main findings and their comparisons in context. Lastly, Section 5 provides the main findings and illustrates future challenges.