Optimism and pessimism analysis using deep learning on COVID-19 related twitter conversations.

ABSTRACT: This paper proposes a new deep learning approach to better understand how optimistic and pessimistic feelings are conveyed in Twitter conversations about COVID-19. A pre-trained transformer embedding is used to extract the semantic features and several network architectures are compared. Model performance is evaluated on two new, publicly available Twitter corpora of crisis-related posts. The best performing pessimism and optimism detection models are based on bidirectional long- and short-term memory networks. Experimental results on four periods of the COVID-19 pandemic show how the proposed approach can model optimism and pessimism in the context of a health crisis. There is a total of 150,503 tweets and 51,319 unique users. Conversations are characterised in terms of emotional signals

SUBMITTER: Blanco G 

PROVIDER: S-EPMC9758015 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature