Positive, negative, and neutral tweets concerning COVID-19 have all lately increased in volume. The broad variety of themes covered by tweets encouraged investigators to use sentiment analysis to assess the public`s response to COVID-19. Conventional sentiment analysis algorithms can only assess polarity, categorizing tweets as positive, negative, or neutral. Logistic Regression sentiment analysis, BLSTM sentiment analysis, and LSTM sentiment analysis are all employed to identify the sentiment of tweets at this advanced phase of the intended research effort. While the offered research methodologies may be used across domains, they are particularly well-suited to detecting emotional expressions in social media situations. With the exception of the sentiment analysis approach, the pretreatment and subsequent operations will be the same despite the employment of three separate algorithms. Using the identical processing processes, the three recommended sentiment analysis algorithms will be compared. Furthermore, the proposed analysis has a broad range of practical applications since it gives a public opinion to government officials or even health officials and assists them in basing their judgments on that viewpoint.

Positive, Negative, Covid-19, Tweets, Lockdown, LSTM, BLSTM.

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