使用社交媒体数据的情绪分析预测传染病的预测
Youngchul Song1, Byungun Yoon1
1Department of Industrial & Systems Engineering, Dongguk University, Jung-gu, Seoul, South Korea.
PloS one
|September 4, 2024
概括
通过分析社交媒体的意见,预测传染病的传播得到了增强. 这项研究使用情绪分析和机器学习来改进病例数预测,显示了准确度的显著提高.
科学领域:
- 流行病学 流行病学
- 计算社会科学 计算社会科学
- 数据科学数据科学数据科学
背景情况:
- 传染病风险不断上升,需要改进预测模型.
- 现有的模型往往忽视了公众情绪和社会数据对疾病传播的影响.
- 在传染病预测中,社交媒体的定性意见未得到充分利用.
研究的目的:
- 通过社交媒体情绪分析,提出一种用于预测传染病病例的新方法.
- 将论挖掘与机器学习技术相结合,以提高疾病预测.
- 评估社会数据情绪对传染病预测准确性的影响.
主要方法:
- 收集社交媒体数据并使用Word2Vec.ec.构建的专用字典进行情绪分析.
- 计算了每天的积极和消极情绪极性.
- 开发并应用深度学习模型,包括DNN,LSTM和GRU,结合情绪极性进行预测.
主要成果:
- 与没有情绪分析的模型相比,采用论挖掘的拟议方法在LSTM模型中提高了1.12%,在GRU模型中提高了3%的预测准确度.
- 对社会数据的情绪分析为传染病预测提供了有价值的定性见解.
- 机器学习模型在利用社会情绪进行预测方面表现出有效性.
结论:
- 社交媒体情绪分析是提高传染病病例预测准确度的宝贵工具.
- 整合定性社会数据为流行病学预测提供了新的视角.
- 拟议的方法突出了利用公众论进行公共卫生监测的潜力.
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