In the Skip-gram strategy, the network is trained to predict the other words in the window based on the central word. In both strategies, the learning signal for the network (and hence the information that is encoded in the embeddings the network generates) is the likelihood of one word co-occurring in the surrounding context of another word (i.e., within the same window). In the present paper, we use the Skip-gram model, which has shown better performance in semantic tasks [24]. Psychological resilience, as a general term, deals with how people manage stress and how they recover from traumatic events, encouraging constructive growth and promoting an optimistic outlook on the future [25]. Evidence suggests that when resilience-based abilities are applied to people's lives, they have many advantages (for example, a carry-over effect on other life domains) [26]. Resilience may be improved with deliberate practice; it is not necessary to be born with it [27]. However, within the research community, there is a lack of a unified definition for the concept [28]. This lack of consensus in definition can also be linked to the lack of consensus on how the concept should be operationalized in order to address community disasters [29]. As recently reported [30], positive and negative emotions have varied effects on developing a resilient attitude. People who go through higher levels of positive emotions (i.e., gratitude, compassion, love, relief, hope, calm, or admiration) exhibit a higher degree of resilience, whereas those who feel high levels of negative emotions (i.e., anger, loneliness, boredom, fear, anxiety, confusion, sadness) are associated with poorer resilience. Typically, large general-purpose corpora (e.g., Wikipedia dumps with 3 billion words [31]) are used to learn word embeddings. Nevertheless, in this work, we hypothesized that word embeddings could be extracted from publicly available social media, using open source software, in sufficient numbers such that the embeddings (1) are relevant to provide meaningful context to specific emotions specifically linked to an ill-defined domain such as psychological resilience (2) verifiable by sound theoretical semantic tests such as the Battig and Montague norm [32] (3) consistent with current related scientific publications and (4) offering the possibility of providing actionable knowledge to on-field specialists. Therefore the objectives of this work are to (1) train a model (W2V) for creating word associations (also known as embeddings) using a publicly available dataset (a subreddit on Coronavirus from January 2020 to July 2021, a period where emotions were exacerbated) and open access software (R libraries) able to retrieve meaningful closest terms. (2) Such a W2V model aims to be formally validated using the semantic categorization test by means of an updated and expanded version [33] of the Battig and Montague norm, with 65 categories; for each category, the silhouette coefficient of the model will be computed. As a complementary validation step, the extensive scientific literature is aimed to be included, supporting our findings. (3) We will then run W2V to discover the context for seven specific positive and seven negative emotions recently reported as related to resilience during the COVID-19 pandemic, and (4) such specific context will be supported using related scientific publications. The article is organized as follows. A literature review is presented in Section 2. Materials and methods are introduced in Section 3. In Section 4, we initially report a descriptive analysis of the sample; we then present the results of our W2V model at three different levels (toy-example analogies, representative terms from a COVID-19 glossary, and resilience related terms) for both the COVID-19 glossary and resilience related terms. We support our findings with extensive scientific literature and then discuss performance using the Battig and Montague evaluation. The discussion and limitations are presented in Section 5. Lastly, in Section 6, we conclude the paper.