## 5 Discussion  
In this study, we proposed social media (particularly a Reddit subforum) as a connection between word associations (also known as embeddings) and emotion research. Although they both share context as a critical component, to our best knowledge, word em \begin{table}
\begin{tabular}{c c} \hline \hline
**Search Term** & **Closest Terms** \\ \hline \multirow{3}{*}{anger} & frustration (0.471), confusion (0.474), tension (0.555), chaos (0.592), dishonesty (0.630), hostility (0.617), bureaucracy (0.625), fear (0.608), drought (0.579), outcry (0.598), outrage (0.618), \\ \hline \multirow{3}{*}{loneliness} & profound (0.607), addiction (0.635), neuropsychiatric (0.630), opioid (0.658) \\ \hline \multirow{3}{*}{boredom} & spotify (0.615), playlists (0.599), song (0.593), halo (0.596), fortrite (0.626), meditation (0.615), illustration (0.631), piano (0.632), \\ \hline \multirow{3}{*}{fear} & conspiracies (0.587), xenophobia (0.612), racism (0.621), burnout (0.623), starving (0.636), sadness (0.532) \\ \hline \multirow{3}{*}{anxiety} & stress (0.251), depression (0.431), meditation (0.578), obsessive (0.532), ideation (0.511), cope (0.529), coping (0.514), tips (0.570) \\ \hline \multirow{3}{*}{confusion} & anger (0.474), frustration (0.546), chaos (0.543), distrust (0.561), tension (0.532), worries (0.522), doubts (0.533) \\ \hline \multirow{3}{*}{sadness} & Disbelief (0.433), downfall (0.544), dislike (0.541), downvotes (0.541), fear (0.532), boredom (0.533), together (0.544), spinning (0.541) \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Negative emotions and the obtained closest terms. Figure 4: Dendrograms and heatmap for the closest terms to two positive (hope and gratitude) and two negative (anger and anxiety) emotions. beddings have rarely been used in the field of emotion research. Furthermore, COVID-19 created a unique opportunity for doing it. Therefore, we trained a model for producing word embeddings using a publicly accessible dataset (a Coronavirus subreddit) and open-source tools (R libraries) capable of retrieving relevant content (closest words). This content was formally validated using a standard tool and supported by public evidence (scientific publications), and applied to the discovery of context for seven specific positive and seven negative emotions recently reported as related to resilience during the COVID-19 pandemic. Our results confirmed our three initial hypotheses: word embeddings may be recovered in sufficient numbers from public domain-specific social media for the embedding to (1) be relevant to offer meaningful context to specific emotions, (2) be verifiable by sound theoretical semantic tests such as the Battig and Montague norm, and (3) be consistent with recent related publications, in spite of working with a relative \"small\" number of Reddit titles. In relation to our fourth hypothesis (provide actionable knowledge to on-field specialists), current research reporting on the COVID-19 pandemic concluded that developing a resilient mentality differs depending on whether positive or negative emotions are present. Higher levels of positive emotions are correlated with higher levels of resilience, whereas high levels of negative emotions are associated with lower levels of resilience [30]. We associated seven positive and seven negative emotions to experienced situations. Specialists could therefore promote actions encouraging participation in activities related to positive emotions. For example, as shown in Table 6, \"gratitude\" and \"admiration\" were shown by means of activities taking place worldwide. People congregated on balconies while confined to their apartments to acclaim medical personnel working on the front lines, as well as to sing or take part in impromptu flash mobs [96]. Calm and compassion were associated with meditation and mindfulness. Hope was associated with humor, smiling, laughing, fun, and funny. When analyzing negative emotions, we found racism and xenophobia mainly related to fear.