NIT COVID-19 at WNUT-2020 Task 2: Deep Learning Model RoBERTa for Identify Informative COVID-19 English Tweets
This work addresses the need for filtering relevant COVID-19 information from social media, but it is incremental as it applies an existing method to a specific dataset.
The paper tackled the problem of automatically identifying informative COVID-19 English tweets, such as those reporting cases or locations, and achieved an F1-score of 89.14% using a pre-trained RoBERTa model with pre-processing and hyperparameter tuning.
This paper presents the model submitted by the NIT_COVID-19 team for identified informative COVID-19 English tweets at WNUT-2020 Task2. This shared task addresses the problem of automatically identifying whether an English tweet related to informative (novel coronavirus) or not. These informative tweets provide information about recovered, confirmed, suspected, and death cases as well as the location or travel history of the cases. The proposed approach includes pre-processing techniques and pre-trained RoBERTa with suitable hyperparameters for English coronavirus tweet classification. The performance achieved by the proposed model for shared task WNUT 2020 Task2 is 89.14% in the F1-score metric.