Know an Emotion by the Company It Keeps: Word Embeddings from Reddit/Coronavirus
-
Published:2023-05-31
Issue:11
Volume:13
Page:6713
-
ISSN:2076-3417
-
Container-title:Applied Sciences
-
language:en
-
Short-container-title:Applied Sciences
Author:
García-Rudolph Alejandro123ORCID, Sanchez-Pinsach David123, Frey Dietmar4, Opisso Eloy123ORCID, Cisek Katryna5, Kelleher John D.5ORCID
Affiliation:
1. Department of Research and Innovation, Institut Guttmann, Institut Universitari de Neurorehabilitació Adscrit a la UAB, 08027 Badalona, Spain 2. Universitat Autònoma de Barcelona, Bellaterra (Cerdanyola del Vallès), 08193 Barcelona, Spain 3. Fundació Institut d’Investigació en Ciències de la Salut Germans Trias i Pujol, 08916 Badalona, Spain 4. CLAIM Charité Lab for AI in Medicine, Charité Universitätsmedizin Berlin, 10117 Berlin, Germany 5. Information, Communication and Entertainment Research Institute, Technological University Dublin (TU Dublin), D7 EWV4 Dublin, Ireland
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.
Funder
PRECISE4Q Personalized Medicine by Predictive Modelling in Stroke for Better Quality of Life—European Union’s Horizon 2020 research and innovation program
Subject
Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science
Reference107 articles.
1. Melton, C.A., White, B.M., Davis, R.L., Bednarczyk, R.A., and Shaban-Nejad, A. (2022). Fine-tuned Sentiment Analysis of COVID-19 Vaccine-Related Social Media Data: Comparative Study. J. Med. Internet Res., 24. 2. Reddit–Dive into Anything. Founded: June 23, 2005, Medford, Massachusetts, United States (2023, March 19). Available online: https://www.reddit.com/. 3. What social media told us in the time of COVID-19: A scoping review;Tsao;Lancet Digit. Health,2021 4. White, B.M., Melton, C., Zareie, P., Davis, R.L., Bednarczyk, R.A., and Shaban-Nejad, A. (2023). Exploring celebrity influence on public attitude towards the COVID-19 pandemic: Social media shared sentiment analysis. BMJ Health Care Inform., 30. 5. Al-Garadi, M.A., Yang, Y.C., and Sarker, A. (2022). The Role of Natural Language Processing during the COVID-19 Pandemic: Health Applications, Opportunities, and Challenges. Healthcare, 10.
Cited by
3 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
|
|