Comparison of Media Sources for COVID-19 by Machine Learning Sentiment Analysis

Abstract

There is literature on Machine Learning Sentiment Analysis (MLSA) during the COVID-19 pandemic, however, to the best of our knowledge, there has been little to no research investigating the effectiveness of different internet media sources for the prediction of population-level sentiment; Twitter is currently the most often used in MLSA research. This study conducts COVID-19 related MLSA on various internet media sources to determine the relative effectiveness in each for mining public sentiment. The natural language processing is achieved through a long short-term memory (LSTM) neural network. By comparing trends of sentiment between social medias Twitter and Reddit, and news source USA Today with that of a control survey by data intelligence company Morning Consult, it is found Twitter has the lowest deviation in trends to that of the control. Assuming the objectivity of the control, Twitter is a better indicator of public sentiment as compared to Reddit and USA Today, capable for future applications of MLSA, especially when used in tandem with pre-existing surveys. This work helps advance research in MLSA with implications in informed decisions on fighting/recovering from COVID-19, flattening future potential pandemic curves, and indicating trends in public psychological and mental health.