NLP @ NISER : Classification of COVID 19 tweets containing symptoms (2021)

Abstract: In this paper, we describe our approaches for task six of Social Media Mining for Health Applications (SMM4H) shared task in 2021. The task is to classify twitter tweets containing COVID-19 symptoms in three classes (self-reports, non-personal reports & literature/news mentions). We implemented BERT and XLNet for this text classification task. Best result was achieved by XLNet approach, which is F1 score 0.94, precision 0.9448 and recall 0.94448. This is slightly better than the average score, i.e. F1 score 0.93, precision 0.93235 and recall 0.93235.

## introduction in the beginning of the covid-19 pandemic and even now, with variety of strains, caused great deal of information deficiency about symptoms as reported by people affected by it. as this disease is highly contagious and rapidly changing, one of the best sources for the live information is on the social media. there can be multiple sources of symptoms information on social media such as news/scientific articles (facts), other people's account (second or third person statements) and self report (first person statements). in this paper we will discuss our approach as a team participating in smm4h @xcite shared task 6 related to the classification of such information from social media platform like twitter. we will be looking at using pre-trained nlu models like bert @xcite and xlnet @xcite for this task. ## experiments we perform several experiments on cleaned and uncleaned data. we explore both base and large versions of bert and xlnet along with different training methods, fine-tuning and retraining the whole model, scores of which are mentioned in ## results and conclusion we can have the following observations from