Sentimental Analysis of Lockdown in India During Covid-19

Sentiment analysis has quickly become one of the most prominent fields of natural language processing due to the spectacular increase in Internet usage (NLP). For a variety of situations, sentiment analysis can successfully extract the text’s inferred emotion. During the COVID-19 epidemic, a tremendous amount of information is being received and shared on social media. Mining such content to assess people’s attitudes may substantially improve decision-making in order to maintain control of the situation. In this study, sentiment analysis of tweets has been completed using NLP and machine learning classifiers. The data have been scraped from Twitter, annotated with Text Blob, and pre-processed using the natural language processing tools provided into Python. The study focuses on the sentiment analysis of people during COVID-19 pandemic in India. RNN is used for obtaining a tolerable level of emotion prediction. The proposed method categorized emotions into three categories such as positive, neutral and negative, and obtained an accuracy rate of 90% with a prediction that people support the government’s decision to impose the lockdown during the spread of COVID-19.