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.

翻译：最近关于哈曼斯克拉尔霍乱爆发的社交媒体帖子突显了人们对这类事件所经历的各种情感。由于对疾病的了解程度不同，人们的观点差异很大。现有关于霍乱的研究缺乏对情感分类的探讨。本研究旨在分析社交媒体帖子中与霍乱相关的情感表达。提取并预处理了一个包含23,000条帖子的数据集。应用Python自然语言工具包（NLTK）情感分析库确定每条文本的情感重要性。此外，采用机器学习（ML）模型进行情感分类，包括长短期记忆网络（LSTM）、逻辑回归、决策树以及基于变换器的双向编码器表示（BERT）模型。研究结果表明，LSTM分类准确率最高，达到75%。情感分类为深入理解霍乱对社会的影响提供了有前景的工具。本研究的发现可能有助于制定有效的公共卫生策略干预措施。