基于机器学习的自然语言处理用于霍乱 X 数据集上的情绪分类
Machine Learning-based NLP for Emotion Classification on a Cholera X Dataset
AI总结：
针对霍乱相关社交媒体情绪研究不足，本文构建 23,000 条帖子数据集，比较 NLTK 与 LSTM、逻辑回归、决策树和 BERT，发现 LSTM 以 75% 准确率表现最佳。
摘要：
最近关于 Hammanskraal 霍乱疫情的社交媒体帖子凸显了人们在应对此类事件时经历的多样情绪。人们观点的广度因其对该疾病的知识和信息了解程度而存在很大差异。已有关于霍乱的记录研究缺乏对情绪分类的调查。本研究旨在考察关于霍乱的社交媒体帖子中所表达的情绪。研究提取并预处理了一个包含 23,000 条帖子的数据集。研究使用 Python Natural Language Toolkit（NLTK）情感分析器库来确定每段文本的情绪意义。此外，研究还将机器学习（ML）模型用于情绪分类，包括长短期记忆（LSTM）、逻辑回归、决策树以及来自 Transformers 的双向编码器表示（BERT）模型。研究结果表明，LSTM 取得了最高准确率，为 75%。情绪分类提供了一种有前景的工具，可用于更深入地理解霍乱对社会的影响。本研究的发现可能有助于在公共卫生策略中制定有效的干预措施。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
