Recent years have seen a rise in the significance of sentiment analysis as a direct result of the explosion in the amount of material available online. The practice of analyzing textual data created on social media sites such as Facebook and Twitter using a natural language processing approachis called sentiment analysis. Since the beginning of the COVID19 pandemic, several postings, including videos and text messages, have been uploaded on the social media platform in order to provide real-time updates on the progression of the pandemic across the world's nations. The term refers to the practice of analyzing the wordbased data created by social media platforms, which can be accessed, retrieved, and evaluated with relative ease. Most of the published research relating to COVID-19 issue theories were surveys of people's thoughts and ideas, and they explored the influence that the pandemic had on their life. This was because COVID-19 became prevalent after it was discovered. A very small number of researchers used a machine learning strategy for the task of analyzing the sentiment of social media. The rapid spread of the illness has resulted in a significant rise in the number of posts and comments made by users of social media, and these expressions of opinion cover a wide range of topics. The topic of sentiment analysis is discussed in this article, with the primary emphasis being placed on the categorization of the feelings sent by users in tweets originating from Twitter that are associated with COVID-19. Using deep learning techniques (CNN, LSTM, CNN + Bi-LSTM), as well as CNN + Bi-LSTM +CNN (CBC) a deep learning-based ensemble model, we were able to categorize the attitudes as positive, neutral, or negative. In comparison to the typical machine learning models, the BiLSTM technique has obtained more accuracy (0.98), when it comes to the categorization of Twitter sentiment. Based on the findings of the investigation as a whole, we are able to draw the conclusion that individuals have higher levels of optimism and confidence toward the recovery from the COVID-19 pandemic. A study of this kind will assist those responsible for making policies and decisions in meeting the requirements of the public in an acceptable manner.