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|a Text processing methods like lexicon-based unsupervised approaches play important roles to quantify public opinions in the textual domain. While these methods have benefit to directly generate sentiment scores from text data based on the word-intensity scores, they perform poorly with shorter unstructured texts like tweets. Besides, these lexicon models often produce poor accuracy with the human annotated datasets. To overcome these limitations of lexicon models, a new hybrid approach has been proposed. This new approach capitalizes the prediction capabilities of two supervised machine learning models to revise the lexicon scores using Bipolar sigmoid function that confirm better accuracy in the sentiment analysis. Three pre-annotated datasets have been used to verify lexicon-based models and the proposed hybrid method. Finally, the proposed method has been applied to the corona-induced tweets, which were collected from Japan, USA, UK, and Australia during January–June 2020. Several sentiment and emotion timeseries have been constructed and evaluated using statistical analysis against three events namely the first declaration of lockdown (FLD), the first declaration of the economic support package (FEP), and the first death-severity (FDS) event. Results showed the significant reduction of the mean negative polarity (meanNeg) in the USA and the significant increase of the ratio between positive and negative tweets (pnRatio) in the UK after the FLD event. The UK people also showed significant reduction of the mean polarity (meanPol) after the FLD and FDS events, respectively. On the other hand, the sadness emotion in the UK after the FEP, the anger and sadness emotions in Australia after the FDS event, and the surprise emotion in the UK after the FDS event have shown significant changes. However, no emotional variables after the FLD event and no sentiment variables after the FEP event have shown any impact among the people in any of the four countries. Surpringly, no events including government responses (FLD, FEP) to COVID-19 showed significant changes to the emotions of Japanese people. Our results can help leader’s policy decisions and can also perform more accurate prediction of the disaster driven public sentiments. © 2022, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.
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