The free flow of information has been accelerated by the rapid development of social media technology. There has been a significant social and psychological impact on the population due to the outbreak of Coronavirus disease (COVID-19). The COVID-19 pandemic is one of the current events being discussed on social media platforms. In order to safeguard societies from this pandemic, studying people's emotions on social media is crucial. As a result of their particular characteristics, sentiment analysis of texts like tweets remains challenging. Sentiment analysis is a powerful text analysis tool. It automatically detects and analyzes opinions and emotions from unstructured data. Texts from a wide range of sources are examined by a sentiment analysis tool, which extracts meaning from them, including emails, surveys, reviews, social media posts, and web articles. To evaluate sentiments, natural language processing (NLP) and machine learning techniques are used, which assign weights to entities, topics, themes, and categories in sentences or phrases. Machine learning tools learn how to detect sentiment without human intervention by examining examples of emotions in text. In a pandemic situation, analyzing social media texts to uncover sentimental trends can be very helpful in gaining a better understanding of society's needs and predicting future trends. We intend to study society's perception of the COVID-19 pandemic through social media using state-of-the-art BERT and Deep CNN models. The superiority of BERT models over other deep models in sentiment analysis is evident and can be concluded from the comparison of the various research studies mentioned in this article.

翻译：社交媒体的快速发展加速了信息的自由流动。新冠肺炎疫情的爆发对公众造成了显著的社会和心理影响。COVID-19疫情是当前社交媒体平台上讨论的热点事件之一。为保护社会免受疫情影响，研究社交媒体上民众的情感至关重要。由于推文等文本的特殊性质，对其的情感分析仍具有挑战性。情感分析是一种强大的文本分析工具，能够从非结构化数据中自动检测和分析观点与情感。情感分析工具通过检查各类来源的文本（包括电子邮件、调查问卷、评论、社交媒体帖子及网络文章）来提取语义信息。为评估情感，研究者采用自然语言处理与机器学习技术，通过为句子或短语中的实体、主题、议题及类别分配权重来实现分析。机器学习工具通过分析文本中的情感示例，无需人工干预即可学习情感检测方法。在疫情情境下，分析社交媒体文本以揭示情感趋势，有助于更深入地理解社会需求并预测未来趋势。本研究拟采用最先进的BERT与深度CNN模型，通过社交媒体分析公众对COVID-19疫情的认知。通过比较本文所述的多项研究可得出结论：BERT模型在情感分析任务中显著优于其他深度模型。