论文标题
在COVID-19大流行期间，在Twitter上利用自然语言处理来挖掘问题
Leveraging Natural Language Processing to Mine Issues on Twitter During the COVID-19 Pandemic
论文作者
论文摘要
最近的全球冠状病毒疾病爆发（Covid-19）已蔓延到全球各个角落。国际旅行禁令，恐慌购买以及对自我汇率的需求是这个新时代带来的许多其他社会挑战。 Twitter平台已在各种公共卫生研究中使用，以确定有关本地和全球范围内事件的公众舆论。为了了解公众对大流行的关注和回应，该系统可以利用机器学习技术过滤无关的推文并确定在Twitter等社交媒体平台上讨论的重要主题。在这项研究中，我们构建了一个系统，以确定整个2020年1月1日至2020年4月30日的相关推文，并探索了主题建模，以确定我们数据集中此期间最讨论的主题和主题。此外，我们分析了主题在此大流行期间发生的事件的时间变化。我们发现八个主题足以识别我们的语料库中的主题。这些主题描述了时间趋势。随着时间的流逝，主要主题会有所不同，并与与COVID-19大流行有关的事件保持一致。
The recent global outbreak of the coronavirus disease (COVID-19) has spread to all corners of the globe. The international travel ban, panic buying, and the need for self-quarantine are among the many other social challenges brought about in this new era. Twitter platforms have been used in various public health studies to identify public opinion about an event at the local and global scale. To understand the public concerns and responses to the pandemic, a system that can leverage machine learning techniques to filter out irrelevant tweets and identify the important topics of discussion on social media platforms like Twitter is needed. In this study, we constructed a system to identify the relevant tweets related to the COVID-19 pandemic throughout January 1st, 2020 to April 30th, 2020, and explored topic modeling to identify the most discussed topics and themes during this period in our data set. Additionally, we analyzed the temporal changes in the topics with respect to the events that occurred during this pandemic. We found out that eight topics were sufficient to identify the themes in our corpus. These topics depicted a temporal trend. The dominant topics vary over time and align with the events related to the COVID-19 pandemic.
