An improved machine learning technique for identify informative COVID-19 tweets

Abstract

Twitter users are increasingly using the platform to share information, particularly in the case of disease outbreaks such as COVID-19. It's difficult to find informative tweets about coronavirus on Twitter. Recognizing tweets associated with disease evaluation in social media is a critical endeavour because it is a subset of associated data. Existing works rely solely on subject identification, vocabulary construction, idea extraction, polarity detection, descriptive Terms, and disease-related statistical characteristics, resulting in a lack of precision in detecting tweet content. To solve this problem, this study used parts of speech tags and high-resolution graphics. To address this issue, we proposed an IPSH (Informative POS statistical High Frequency) model for predicting COVID-19 tweet content that incorporates parts of speech tags and high-frequency words as features into the existing machine learning model. The model was found to be more efficient when compared to baseline machine learning models using the Twitter COVID-19 disease dataset.