PreprintArticleVersion 1Preserved in Portico This version is not peer-reviewed
ML-SocMedEmot: Machine Learning Event-based Social Media Emotion Detection Proactive Framework Addressing Mental Health: A Novel Twitter Dataset and Case Study of COVID-19
Version 1
: Received: 14 July 2023 / Approved: 14 July 2023 / Online: 17 July 2023 (10:16:20 CEST)
How to cite:
Ismail, L.; Shahin, N.; Materwala, H.; Hennebelle, A.; Frermann, L. ML-SocMedEmot: Machine Learning Event-based Social Media Emotion Detection Proactive Framework Addressing Mental Health: A Novel Twitter Dataset and Case Study of COVID-19. Preprints2023, 2023071098. https://doi.org/10.20944/preprints202307.1098.v1
Ismail, L.; Shahin, N.; Materwala, H.; Hennebelle, A.; Frermann, L. ML-SocMedEmot: Machine Learning Event-based Social Media Emotion Detection Proactive Framework Addressing Mental Health: A Novel Twitter Dataset and Case Study of COVID-19. Preprints 2023, 2023071098. https://doi.org/10.20944/preprints202307.1098.v1
Ismail, L.; Shahin, N.; Materwala, H.; Hennebelle, A.; Frermann, L. ML-SocMedEmot: Machine Learning Event-based Social Media Emotion Detection Proactive Framework Addressing Mental Health: A Novel Twitter Dataset and Case Study of COVID-19. Preprints2023, 2023071098. https://doi.org/10.20944/preprints202307.1098.v1
APA Style
Ismail, L., Shahin, N., Materwala, H., Hennebelle, A., & Frermann, L. (2023). ML-SocMedEmot: Machine Learning Event-based Social Media Emotion Detection Proactive Framework Addressing Mental Health: A Novel Twitter Dataset and Case Study of COVID-19. Preprints. https://doi.org/10.20944/preprints202307.1098.v1
Chicago/Turabian Style
Ismail, L., Alain Hennebelle and Lea Frermann. 2023 "ML-SocMedEmot: Machine Learning Event-based Social Media Emotion Detection Proactive Framework Addressing Mental Health: A Novel Twitter Dataset and Case Study of COVID-19" Preprints. https://doi.org/10.20944/preprints202307.1098.v1
Abstract
Global rapidly evolving events, e.g., COVID-19, are usually followed by countermeasures and policies. As a reaction, the public tends to express their emotions on social media platforms. Therefore, predicting emotional responses to events is critical to put a plan to avoid risky behaviors. This paper proposes a machine learning-based framework to detect public emotions based on social media posts in response to specific events. It presents a precise measurement of population-level emotions which can aid governance in monitoring public response and guide it to put in place strategies such as targeted monitoring of mental health, to react to a rise in negative emotions in response to lockdowns, or information campaigns, for instance in response to elevated rates of fear in response to vaccination programs. We evaluate our framework by extracting 15,455 tweets. We annotate and categorize the emotions into 11 categories based on Plutchik’s study of emotion and extract the features using a combination of Bag of Words and Term Frequency-Inverse Document Frequency. We filter 813 COVID-19 vaccine-related tweets and use them to demonstrate our framework’s effectiveness. Numerical evaluation of emotions prediction using Random Forest and Logistic Regression shows that our framework predicts emotions with an accuracy up to 95%.
Keywords
Artificial Intelligence; COVID-19; Digital Media, Emotions Detection; Machine Learning; Medical Informatics; Mental Health; Natural language Processing; SARS-COV-2; Social Media; Supervised Learning; Vaccination
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
