Sentiment Analysis of Lockdown in India During COVID-19 A Case Study on Twitter

With the rapid increase in the use of the Internet, sentiment analysis has become one of the most popular fields of natural language processing (NLP). Using sentiment analysis, the implied emotion in the text can be mined effectively for different occasions. People are using social media to receive and communicate different types of information on a massive scale during COVID-19 outburst. Mining such content to evaluate people's sentiments can play a critical role in making decisions to keep the situation under control.

In this work, the sentiment analysis of tweets has been performed using NLP and machine learning classifiers. Data have been extracted from Twitter, annotated using Text-Blob and preprocessed using the natural language tool kit provided by the Python. RNN is used for sentiment classification in this project. This study concludes that the majority of Indian citizens are supporting the decision of the lockdown implemented by the Indian government during corona outburst.