  The COVID-19 epidemic has had a great impact on social media conversation,
especially on sites like Twitter, which has emerged as a hub for public
reaction and information sharing. This paper deals by analyzing a vast dataset
of Twitter messages related to this disease, starting from January 2020. Two
approaches were used: a statistical analysis of word frequencies and a
sentiment analysis to gauge user attitudes. Word frequencies are modeled using
unigrams, bigrams, and trigrams, with power law distribution as the fitting
model. The validity of the model is confirmed through metrics like Sum of
Squared Errors (SSE), R-squared ($R^2$), and Root Mean Squared Error (RMSE).
High $R^2$ and low SSE/RMSE values indicate a good fit for the model. Sentiment
analysis is conducted to understand the general emotional tone of Twitter users
messages. The results reveal that a majority of tweets exhibit neutral
sentiment polarity, with only 2.57\% expressing negative polarity.
