评估情绪分析模型:在COVID-19阶段对疫苗接种推特进行比较分析,利用DistilBERT增强洞察力
Renuka Agrawal1, Mehuli Majumder1, Ishita Yadav1
1Symbiosis Institute of Technology - Pune Campus, Symbiosis International (Deemed University), Pune, India.
MethodsX
|June 18, 2025
概括
这项研究使用机器学习和自然语言处理来分析从Twitter数据中对COVID-19疫苗接种的公众情绪. 先进的技术改善了对卫生危机期间疫苗犹和公众论的理解.
科学领域:
- 计算社会科学 计算社会科学
- 公共卫生信息学 公共卫生信息学
- 医疗保健中的人工智能
背景情况:
- 像Twitter这样的社交媒体平台对于了解卫生危机期间的论至关重要.
- 衡量公众对COVID-19疫苗接种的情绪对于有效的公共卫生战略至关重要.
- 错误信息和对疫苗的犹需要先进的分析工具来分析情绪.
研究的目的:
- 利用推特数据调查公众对COVID-19疫苗接种的情绪.
- 评估各种机器学习模型的性能和情感分类的嵌入技术.
- 通过准确的情绪分析,为内容调节和错误信息控制政策提供信息.
主要方法:
- 用线性SVC,随机森林,GBM,XGBoost和AdaBoost模型对TF-IDF和Word2Vec嵌入技术进行比较分析.
- 利用训练测试分割 (70-30和80-20) 来评估模型在不平衡和杂的情绪数据上的表现.
- 使用DistilBERT进行伪标签,以提高推特中情绪分类的准确性.
主要成果:
- 该研究证明了自动注释,混合建模和嵌入策略分析社交媒体数据的有效性.
- 基于DistilBERT的伪标签改善了语义细微差别的捕获,以便更有效地分类情绪.
- 不同的机器学习模型表现出不同的性能,突出了模型选择对情绪分析的重要性.
结论:
- 先进的自然语言处理和机器学习技术为流行病期间的公众情绪提供了宝贵的见解.
- 通过社交媒体分析了解疫苗犹,可以塑造有针对性的公共卫生沟通策略.
- 整合复杂的NLP工具对于理解和响应卫生紧急情况中的公众情绪至关重要.
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