  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.
