  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.
