Revue de l'Information Scientifique et Technique
Volume 27, Numéro 2, Pages 18-23
2023-11-08
Classifying Covid-19 Related Tweets For Fake News Detection And Sentiment Analysis With Bert-based Models
Authors : Bounaama Rabia . Abderrahim Mohammed El Amine .
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.
Keywords
CERIST ; BERT ; fake news detection ; sentiment analysis
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