COVID-19

People turn to search engines and social media to seek information during
population-level events, such as during civil unrest, disease outbreaks, fires, or flood.
They also tend to participate in discussions and disseminate information and opinions
via social media forums, and smartphone messaging applications. COVID-19 pandemic
was not any different. However, the proper medical awareness and correct information
dissemination is critical during a pandemic. An unprecedented amount of internet traffic

In Indian sub-continent COVID-19 second wave started in early March 2021 and its effect was more lethal than the first wave, the confirmed cases and the death rate was higher than in the first wave. Unlike the national lockdown in 2020, this year different states have started imposing lockdown like restrictions spanning April-June 2021. This paper investigates the sentiments of the people using twitter messages during early period of the second wave. Two-weeks data is manually annotated and several machine learning models were built.

Depressive/Non-depressive tweets  between December 2019 and December 2020 originated largely from India and parts of Indian subcontinent. Sentiment Scores alloted using text blob. Tweets are extracted specifically keeping in mind the top 250 most frequently used negative lexicons and positive lexicons accesed using SentiWord and various research publications.

Tweet Amount : 1.4 Lakhs

Reverse transcription-polymerase chain reaction (RT-PCR) is currently the gold standard in COVID-19 diagnosis. It can, however, take days to provide the diagnosis, and false negative rate is relatively high. Imaging, in particular chest computed tomography (CT), can assist with diagnosis and assessment of this disease. Nevertheless, it is shown that standard dose CT scan gives significant radiation burden to patients, especially those in need of multiple scans.