The COVID-19 pandemic has led to the emergence of social media platforms as crucial channels for the dissemination of information and public opinion. Comprehending the sentiment conveyed in tweets on COVID-19 is of paramount importance for individuals involved in policymaking, crisis management, and public health administration. This study seeks to conduct a comprehensive review of the current BERT and deep CNN models utilized in sentiment analysis of COVID-19 tweets. Additionally, the study aims to propose potential future research directions for the development of a BERT model that is both lightweight and high quality. The BERT model acquires contextual representations of words and effectively captures the intricate semantics of tweets related to COVID-19, whereas the deep CNN captures the hierarchical organization of the tweet embeddings. The performance of the model is exceptional, exceeding the current sentiment analysis methods for tweets related to COVID-19. Our study involves a comprehensive analysis of vast COVID-19 tweet datasets, wherein we establish the efficacy of the BERT-deep CNN models in precisely categorizing the sentiment of COVID-19 tweets in real time. The outcomes of the research offer significant perspectives on the public\\\'s attitudes, supporting decision-makers in comprehending the general viewpoint, detecting disinformation, and guiding emergency response tactics. Additionally, this study serves to enhance the progress of sentiment analysis methodologies within the realm of public health emergencies and establishes a standard for forthcoming investigations in the sentiment analysis of social media data pertaining to COVID-19.

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