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基于词典的全球推特对冠状病毒(COVID-19)影响及应对的情绪分析

Dictionary Based Global Twitter Sentiment Analysis of Coronavirus (COVID-19) Effects and Response.

作者信息

Okango Elphas, Mwambi Henry

机构信息

School of Mathematics and Computer Science, University of KwaZulu-Natal, Pietermaritzburg, South Africa.

出版信息

Ann Data Sci. 2022;9(1):175-186. doi: 10.1007/s40745-021-00358-5. Epub 2022 Jan 20.

Abstract

In December 2019, a new pandemic called the coronavirus began ravaging the world. By May 2020, the pandemic had caused great loss of lives and disrupted the way of lives in more ways than one. The nature of the disease saw several strategies to curb its spread rolled out. These strategies included closing of businesses and borders, restriction of movements and working from home, mask mandate among others. With these measures and the effects, many individuals have taken to the social media to express their frustrations, opinions and how the pandemic is affecting them. This study employs dictionary based method for sentiment polarization from tweets related to coronavirus posted on Twitter. We also examine the co-occurrence of words to gain insights on the aspects affecting the masses. The results showed that mental health issues, lack of supplies were some of the direct effects of the pandemic. It was also clear that the COVID-19 prevention guidelines were well understood by those who tweeted. The results from this study may help governments combat the consequences of COVID-19 like mental health issues, lack of supplies e.g. food and also gauge the effectiveness or the reach of their guidelines.

摘要

2019年12月,一种名为冠状病毒的新型大流行病开始肆虐全球。到2020年5月,这场大流行病已造成巨大的生命损失,并在多个方面扰乱了人们的生活方式。鉴于该疾病的特性,人们推出了多种遏制其传播的策略。这些策略包括关闭企业和边境、限制行动以及居家办公、强制佩戴口罩等。鉴于这些措施及其影响,许多人在社交媒体上表达了他们的沮丧、观点以及这场大流行病对他们的影响。本研究采用基于词典的方法对推特上发布的与冠状病毒相关的推文进行情感极化分析。我们还研究了词汇的共现情况,以深入了解影响大众的各个方面。结果表明,心理健康问题、物资短缺是这场大流行病的一些直接影响。同样明显的是,发推文的人对新冠疫情防控指南有很好的理解。这项研究的结果可能有助于政府应对新冠疫情的后果,如心理健康问题、物资(如食品)短缺等,还能评估其指南的有效性或覆盖面。

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