The rapid spread of COVID-19 has already affected human lives throughout the globe. Governments of different countries have taken various measures, but how they affected people lives is not clear. In this study, a rule-based and a machine-learning based models are applied to answer the above question using public tweets from Japan, USA, UK, and Australia. Two polarity timeseries (meanPol and pnRatio) and two events, namely "lockdown or emergency (LED)" and "the economic support package (ESP)", are considered in this study. Statistical testing on the sub-series around LED and ESP events showed their positive impacts to the people of (UK and Australia) and (USA and UK), respectively unlike Japanese people that showed opposite effects. Manual validation with the relevant tweets showed an agreement with the statistical results. A case study with Japanese tweets using supervised logistic regression classifies tweets into heath-worry, economy-worry and other classes with 83.11% accuracy. Predicted tweets around events re-confirm the statistical outcomes.

翻译：COVID-19的迅速蔓延已经影响到全球人类生活,不同国家政府采取了各种措施,但是它们如何影响人们的生活并不清楚。在这项研究中,运用基于规则的和基于机械学习的模式,用日本、美国、英国和澳大利亚的公开推特回答上述问题。两个极分时间序列(平均Pol和pnRatio)和两个事件,即“锁定或紧急(LED)”和“经济支助一揽子计划(ESP)”,在本研究中加以考虑。LED和ESP事件周围的子系列的统计测试表明,它们分别对(英国和澳大利亚)和(美国和联合王国)人民产生了积极影响,而日本人民则表现出相反的效果。对相关推文的手工验证表明与统计结果一致。对日本的推文进行的一项案例研究,利用监督的后勤回归将推文精确度将推入热工作、经济波动和其他类,83.11%的精确度。关于事件的预测推文证实了统计结果。