Machine Learning-based NLP for Emotion Classification on a Cholera X Dataset

TL;DR

This study applies machine learning models to classify emotions in social media posts about cholera, achieving up to 75% accuracy, to better understand societal impacts and inform public health strategies.

Contribution

It introduces a novel application of ML models for emotion classification specifically on cholera-related social media data, filling a research gap.

Findings

LSTM achieved 75% accuracy in emotion classification.

ML models can effectively analyze public emotions about cholera.

Emotion analysis can inform public health interventions.

Abstract

Recent social media posts on the cholera outbreak in Hammanskraal have highlighted the diverse range of emotions people experienced in response to such an event. The extent of people's opinions varies greatly depending on their level of knowledge and information about the disease. The documented re-search about Cholera lacks investigations into the classification of emotions. This study aims to examine the emotions expressed in social media posts about Chol-era. A dataset of 23,000 posts was extracted and pre-processed. The Python Nat-ural Language Toolkit (NLTK) sentiment analyzer library was applied to deter-mine the emotional significance of each text. Additionally, Machine Learning (ML) models were applied for emotion classification, including Long short-term memory (LSTM), Logistic regression, Decision trees, and the Bidirectional En-coder Representations from Transformers (BERT) model. The results of this study demonstrated that LSTM achieved the highest accuracy of 75%. Emotion classification presents a promising tool for gaining a deeper understanding of the impact of Cholera on society. The findings of this study might contribute to the development of effective interventions in public health strategies.