A Sentiment Analysis to Forecast the Dimensions of Well Being during Pandemic Outbreak Using Machine Learning Algorithms

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Dr. P. Tamije Selvy, Ms. G. Nivedhitha, Ms. K. Saranya


In recent times, our world has been massively affected by a global pandemic. Due to the swift increase in the infection and the death frequency, it has been caused an extensive public health crisis globally and activated some issues such as economic catastrophe, mental and physical worries and so on. In the course of this period, the internet community involvement and dealings rise vigorously and people are able to share one’s perspectives and state of wellbeing. This paper focuses mainly on the dimensions of well-being of every individual during the pandemic outbreak. Initially, from the user-generated content (UGC) on social platform, we can examine the public’s thoughts and sentiments on different aspects such as health grade, concerns and awareness about the pandemic. Further, the analysis is done based on the supervised machine learning approach. The accuracy of the algorithm was around 93%.Through this research work, health organizations and volunteers can better assess and understand the public's needs in order to convey appropriate and effective information. It can also eventually assist in developing health interposition strategies and design operative drives based on public insights.

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