论文标题
COVID-19大流行：使用社交媒体和自然语言处理来确定关键问题
COVID-19 Pandemic: Identifying Key Issues using Social Media and Natural Language Processing
论文作者
论文摘要
COVID-19大流行在许多方面都影响了人们的生活。社交媒体数据可以揭示公众对大流行的看法和经验，也揭示了阻碍或支持抑制全球疾病蔓延的因素。在本文中，我们分析了使用自然语言处理（NLP）技术从六个社交媒体平台收集的COVID-19与19点相关评论。我们从超过100万个随机选择的评论中确定了相关的自以为是的键形及其各自的情感极性（负或正面），然后使用主题分析将它们分为更广泛的主题。我们的结果发现了34个负面主题，其中17个是经济，社会政治，教育和政治问题。还确定了20个积极的主题。我们讨论负面问题，并建议根据积极的主题和研究证据来解决这些问题。
The COVID-19 pandemic has affected people's lives in many ways. Social media data can reveal public perceptions and experience with respect to the pandemic, and also reveal factors that hamper or support efforts to curb global spread of the disease. In this paper, we analyzed COVID-19-related comments collected from six social media platforms using Natural Language Processing (NLP) techniques. We identified relevant opinionated keyphrases and their respective sentiment polarity (negative or positive) from over 1 million randomly selected comments, and then categorized them into broader themes using thematic analysis. Our results uncover 34 negative themes out of which 17 are economic, socio-political, educational, and political issues. 20 positive themes were also identified. We discuss the negative issues and suggest interventions to tackle them based on the positive themes and research evidence.
