Post COVID depression prediction using Twitter data
Date Issued
2023-07
Author(s)
Spirovska, Eva
Dobreva, Jovana
Lucas, Mary
Vodenska, Irena
Chitkushev, Lou
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.
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.
Subjects
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