Post COVID depression prediction using Twitter data

Abstract

This study aims to investigate the prevalence of Post COVID-19 depression by collecting, preprocessing, and analyzing English-language tweets using several natural language processing (NLP) models. Our primary objective is to identify depression-related tweets and develop a machine learning (ML) model for depression prediction. Two datasets are employed for this research: the first is a publicly available depression dataset from Kaggle, and the second is a long covid dataset obtained from Twitter between April 2020 and April 2022. By leveraging NLP techniques and ML algorithms, we analyze these datasets to gain insights into the pandemic’s impact on mental health and identify key features associated with depression. Although the chosen classification model had promising results, it still misclassified certain data, prompting the incorporation of Twitter Account classification. Consequently, this integration resulted in tweets being classified more accurately.