Classifying COVID-19 Related Tweets for Fake News Detection and Sentiment Analysis with BERT-based Models
Computation and Language
2023-04-04 v1
Abstract
The present paper is about the participation of our team "techno" on CERIST'22 shared tasks. We used an available dataset "task1.c" related to covid-19 pandemic. It comprises 4128 tweets for sentiment analysis task and 8661 tweets for fake news detection task. We used natural language processing tools with the combination of the most renowned pre-trained language models BERT (Bidirectional Encoder Representations from Transformers). The results shows the efficacy of pre-trained language models as we attained an accuracy of 0.93 for the sentiment analysis task and 0.90 for the fake news detection task.
Cite
@article{arxiv.2304.00636,
title = {Classifying COVID-19 Related Tweets for Fake News Detection and Sentiment Analysis with BERT-based Models},
author = {Rabia Bounaama and Mohammed El Amine Abderrahim},
journal= {arXiv preprint arXiv:2304.00636},
year = {2023}
}
Comments
CERIST'22: CERIST NLP Challenge 2022, March 29, 2023, Algeria, Algiers