在COVID-19期间印度封锁的情绪分析:Twitter上的一个案例研究
Prasoon Gupta1, Sanjay Kumar1, R R Suman1
1National Institute of Technology Jamshedpur Jamshedpur 831014 India.
概括
根据对12,000多条推文的情绪分析,印度公民在很大程度上支持全国范围的COVID-19封锁. 这项研究使用自然语言处理和机器学习来衡量大流行期间的论.
科学领域:
- 自然语言处理 (NLP) 是一种自然语言处理.
- 机器学习 机器学习
- 社交媒体分析 社交媒体分析
背景情况:
- 由于COVID-19大流行,需要采取前所未有的公共卫生措施,包括全国范围的封锁.
- 在危机期间,社交媒体平台对信息传播和公共话语至关重要.
- 了解公众情绪对于有效的危机管理和政策制定至关重要.
研究的目的:
- 分析印度公民对政府在COVID-19大流行期间实施的全国性封锁的情绪.
- 评估公众对封锁措施的有效性和必要性的意见.
主要方法:
- 在2020年4月5日至17日期间,对12741条与"印度封锁"相关的推文进行了情绪分析.
- 通过Twitter API提取数据,使用TextBlob和VADER词典进行注释.
- 使用Python的自然语言工具包进行预处理,并使用八种机器学习模型进行分类.
主要成果:
- 最高的准确率为84.4%,使用线性SVC分类器与单图实现.
- 情绪分析显示,大多数推特都表达了对封锁的支持.
- 该研究成功地从一个大型推特数据集中挖掘和分类公众情绪.
结论:
- 显著多数印度公民支持政府决定实施全国封锁以控制新冠病毒的传播.
- 对社交媒体数据的情绪分析在卫生危机期间为公众论提供了有价值的见解.
- NLP和机器学习技术是分析关键问题的大规模公众情绪的有效工具.
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