ML-NLPEmot: Machine Learning-Natural Language Processing Event-Based Emotion Detection Proactive Framework Addressing Mental Health

Leila Ismail, Nada Shahin, Huned Materwala, Alain Hennebelle, Lea Frermann

Research output: Contribution to journalArticlepeer-review

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-Natural Language Processing-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 95.5%.

Original languageEnglish
Pages (from-to)144126-144149
Number of pages24
JournalIEEE Access
Volume11
DOIs
Publication statusPublished - 2023

Keywords

  • Artificial intelligence
  • COVID-19
  • emotions detection
  • machine learning
  • medical informatics
  • mental health
  • natural language processing
  • social media
  • supervised learning
  • vaccination

ASJC Scopus subject areas

  • General Computer Science
  • General Materials Science
  • General Engineering

Fingerprint

Dive into the research topics of 'ML-NLPEmot: Machine Learning-Natural Language Processing Event-Based Emotion Detection Proactive Framework Addressing Mental Health'. Together they form a unique fingerprint.

Cite this