Sentiment analysis of tweets and government translations: Assessing China’s post-COVID-19 landscape for signs of withering or booming

Abstract

This article aims to gain insights into the prevailing public sentiment during the policy relaxation period by examining whether the post-COVID-19 landscape reflects signs of withering or booming conditions. Employing methods from natural language processing (NLP) and machine learning (ML), the analysis reveals a predominance of positive sentiment from December 7, 2022 to May 17, 2023, indicative of an optimistic perspective and a potentially flourishing environment. A predictive model based on logistic regression emerges as a notably effective tool for sentiment prediction, suggesting potential utility in predicting future public health crises. A comparison of sentiments in translations by the government aligns with previous research, revealing a less favorable depiction of translated texts compared to the source texts. Furthermore, the commonality index, a measure of group consensus value, surpasses the typical range, while the certainty index, a measure of confidence, slightly falls below the norm. These findings offer valuable insights for policy considerations while highlighting areas for international communication and understanding improvement.