基于自然语言处理的实时监控解决方案,用于社交媒体上的疫苗情绪和犹:系统开发和验证
Liang-Chin Huang1, Amanda L Eiden2, Long He1
1Melax Tech, Houston, TX, United States.
JMIR medical informatics
|June 21, 2024
概括
这项研究开发了一种使用自然语言处理 (NLP) 的实时工具,用于跟踪社交媒体上的疫苗情绪和犹. 该系统分析了数以百万计的讨论,为公共卫生运动提供了洞察力.
科学领域:
- 公共卫生信息学 公共卫生信息学
- 计算社会科学 计算社会科学
- 自然语言处理自然语言处理.
背景情况:
- 疫苗犹是影响疫苗接种的重大公共卫生挑战.
- 追踪犹的传统调查方法有其局限性.
- 实时监测公众情绪对于及时干预至关重要.
研究的目的:
- 开发一种新的自然语言处理 (NLP) 工具,用于实时评估疫苗情绪和犹.
- 在主要的社交媒体平台上分析与疫苗有关的讨论.
- 创建一个仪表板,以可视化疫苗情绪和犹的趋势.
主要方法:
- 从Twitter (X),Reddit和YouTube (2011-2021) 收集和分析了超过8600万个英语讨论.
- 应用NLP算法来分类情绪 (积极,中立,负面) 和疫苗犹,使用世卫组织的3Cs模型 (信心,自满,方便).
- 开发了一个在线仪表板来显示和语境化识别的趋势.
主要成果:
- 性能最好的NLP模型在情绪分类方面达到0.51-0.78的准确度,在犹分类方面达到0.69-0.91的准确度.
- 分析显示,在不同类型的疫苗中,在线疫苗情绪和犹的不同模式.
- 该平台展示了与疫苗情绪和犹有关的在线活动的变化.
结论:
- 创建了一个创新的系统,可以实时分析社交媒体上的疫苗情绪和犹.
- 该工具提供关键的趋势洞察力,以支持公共卫生倡议.
- 这种方法有助于开展专注于提高疫苗接种率和社区健康的运动.
关键词:
在NLP中,我们使用了NLP.我们的态度 态度 态度 态度 态度他们的态度和态度.这是分类分类的分类.犹犹的犹 犹犹的犹机器学习是机器学习.自然语言处理自然语言处理.意见 意见 意见 意见感知 感知 感知 感知感知 感知 感知 感知这是一个前景,一个视角,一个视角.这就是前景,前景,前景.实时跟踪跟踪实时跟踪情绪的情绪是情绪的情绪这些都是感情的感受.社交媒体 社交媒体社交媒体平台 社交媒体平台吸收 吸收 吸收 吸收接种疫苗 接种疫苗接种疫苗 疫苗接种疫苗疫苗 疫苗 疫苗 疫苗疫苗的犹 疫苗的犹疫苗的情绪 疫苗的情绪疫苗,疫苗的使用情况.愿意愿意的愿意愿意的愿意愿意愿意的愿意愿意愿意愿意愿意的愿意愿意
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