PAN-LDA: A latent Dirichlet allocation based novel feature extraction model for COVID-19 data using machine learning.

ABSTRACT: The recent outbreak of novel Coronavirus disease or COVID-19 is declared a pandemic by the World Health Organization (WHO). The availability of social media platforms has played a vital role in providing and obtaining information about any ongoing event. However, consuming a vast amount of online textual data to predict an event's trends can be troublesome. To our knowledge, no study analyzes the online news articles and the disease data about coronavirus disease. Therefore, we propose an LDA-based topic model, called PAN-LDA (Pandemic-Latent Dirichlet allocation), that incorporates the COVID-19 cases data and news articles into common LDA to obtain a new set of features. The generated features are introduced as additional features to Machine learning(ML) algorithms to improve the forecast

SUBMITTER: Gupta A 

PROVIDER: S-EPMC8505021 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature