Document Type

Article

Disciplines

1.2 COMPUTER AND INFORMATION SCIENCE

Publication Details

https://www.mdpi.com/2076-3417/13/11/6713

García-Rudolph, A.; Sanchez-Pinsach, D.; Frey, D.; Opisso, E.; Cisek, K.; Kelleher, J.D. Know an Emotion by the Company It Keeps: Word Embeddings from Reddit/Coronavirus. Appl. Sci. 2023, 13, 6713.

https://doi.org/10.3390/ app13116713

Abstract

Social media is a crucial communication tool (e.g., with 430 million monthly active users in online forums such as Reddit), being an objective of Natural Language Processing (NLP) techniques. One of them (word embeddings) is based on the quotation, “You shall know a word by the company it keeps,” highlighting the importance of context in NLP. Meanwhile, “Context is everything in Emotion Research.” Therefore, we aimed to train a model (W2V) for generating word associations (also known as embeddings) using a popular Coronavirus Reddit forum, validate them using public evidence and apply them to the discovery of context for specific emotions previously reported as related to psychological resilience. We used Pushshiftr, quanteda, broom, wordVectors, and superheat R packages. We collected all 374,421 posts submitted by 104,351 users to Reddit/Coronavirus forum between January 2020 and July 2021. W2V identified 64 terms representing the context for seven positive emotions (gratitude, compassion, love, relief, hope, calm, and admiration) and 52 terms for seven negative emotions (anger, loneliness, boredom, fear, anxiety, confusion, sadness) all from valid experienced situations. We clustered them visually, highlighting contextual similarity. Although trained on a “small” dataset, W2V can be used for context discovery to expand on concepts such as psychological resilience.

DOI

https://doi.org/10.3390/ app13116713

Funder

This research was partially funded by PRECISE4Q Personalized Medicine by Predictive Modelling in Stroke for Better Quality of Life—European Union’s Horizon 2020 research and innovation program under grant agreement No. 777107

Creative Commons License

Creative Commons Attribution-Share Alike 4.0 International License
This work is licensed under a Creative Commons Attribution-Share Alike 4.0 International License.


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