Word frequency and sentiment analysis of twitter messages during Coronavirus pandemic

TL;DR

This study analyzes Twitter messages during the COVID-19 pandemic using word frequency and sentiment analysis, revealing dominant neutral sentiment and modeling word usage with power law distributions.

Contribution

It introduces a combined statistical and sentiment analysis approach specifically applied to COVID-19 related Twitter data, with validation of word frequency models.

Findings

Majority of tweets are neutral in sentiment

Negative sentiment accounts for only 2.57% of tweets

Word usage follows a power law distribution

Abstract

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.