Umesh R. Hodeghatta1 and Sanath V. Haritsa2, 1Northeastern University, USA, 2NU-Sigma U2 Analytics Lab, India
COVID-19 has drastically affected the entire nation. This study involved collecting tweets and analyzing the COVID tweets for August 2020. The aim was to understand whether people have expressed sentiments related to COVID-19 across all the states of the United States and find any correlation between the sentiment tweets and the number of actual cases reported. Around 400000 COVID-19 Twitter data was collected for August 2020 from the primary Twitter database. A simple NLP-based unigram sentiment analyser, a novel approach different from the traditional machine learning approach, was adopted to identify twitter sentiments. The results indicate that tweets related to COVID demonstrate the two types of sentiments, one related to the deaths and the other about the COVID symptoms.
Furthermore, the results show that the sentiments for each category vary from State to State. For example, states of New York, California, Texas are higher tweets sentiments regarding expressing death sentiment, and states of New York, California, Nevada, are higher regarding sentiments of expressing COVID-19 symptoms with an accuracy of 83%. As a part of the research, a new sentiment scorecard was created to provide a sentiment score based on the sentiments of the tweets expressed to the actual reported death cases. The sentiment scores for the ‘symptoms’ class are higher for Maryland, New Jersey, and Oregon, whereas sentiment scores for the 'death' class are higher for Virginia, Delaware, and Hawaii. These sentiment scores indicate that the Twitter users of these states are actively tweeting about symptoms and deaths even though the actual reported cases are less in these states. The analysis results also found no or little correlation between the COVID Tweets and the number of COVID death cases reported across all the states.
COVID 19, Twitter Behaviour, Twitter Analytics, Sentiment Analysis, Big Data Analytics, Business Analytics.