classifying covid-19 related tweets for fake news detection and sentiment analysis with BERT-based models

بونعامة ربيعة; عبد الرحيم محمد الأمين

Article Details

classifying covid-19 related tweets for fake news detection and sentiment analysis with BERT-based models

Authors:

بونعامة ربيعة | عبد الرحيم محمد الأمين

Publisher:

Centre de Recherche sur l'Information Scientifique et Technique

Journal:

RIST Vol.27 Issue 2, pp.18-23

Document Type:

Article

Publication Date:

11/2023

Document Language:

English

Document ID (Org No.):

2001408655

pISSN:

2571-9947

Keywords:

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.

Article Details

classifying covid-19 related tweets for fake news detection and sentiment analysis with BERT-based models

Authors: بونعامة ربيعة | عبد الرحيم محمد الأمين

Publisher: Centre de Recherche sur l'Information Scientifique et Technique

Journal: RIST Vol.27 Issue 2, pp.18-23

Document Type: Article

Publication Date: 11/2023

Document Language: English

Document ID (Org No.): 2001408655

pISSN: 2571-9947

CERIST

BERT

fake news detection

sentiment analysis

Issues

Authors:

بونعامة ربيعة | عبد الرحيم محمد الأمين

Publisher:

Centre de Recherche sur l'Information Scientifique et Technique

Journal:

RIST Vol.27 Issue 2, pp.18-23

Document Type:

Article

Publication Date:

11/2023

Document Language:

English

Document ID (Org No.):

2001408655

pISSN:

2571-9947