Proceedings of the 2nd International Conference on Industry 4.0 and Artificial Intelligence (ICIAI 2021)

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TunRoBERTa: A Tunisian Robustly Optimized BERT Approach Model for Sentiment Analysis

Authors
Chaima Antit1, 3, *, Seifeddine Mechti1, 3, †, Rim Faiz1, 2, †
1Higher institute of management, 41 Av. de la Liberte, Tunis, 2000,Tunis.
2Carthage High Commercial Studies Institute, Rue Victor Hugo, Carthage-Présidence, 2016, Tunisia.
3Operational Research, Decision Making and Process Control Laboratory (Larodec), 41 Av. de la Liberté, Tunis, 2000, Tunis.
†

These authors contributed equally to this work

*Corresponding author(s). E-mail(s): chaimaantit@gmail.com
Corresponding Author
Chaima Antit
Available Online 2 February 2022.
DOI
10.2991/aisr.k.220201.040How to use a DOI?
Keywords
Natural Language Processing; Sentiment Analysis; Deep Learning; Bidirectional Encoder Representations from Transformers; Robustly Optimized BERT Pretraining Approach; Tunisian Dialect; Social media networks
Abstract

Sentiment Analysis has grown in importance and popularity due to the proliferation of microblogging sites and the increase in posted comments, tweets, and posts, as it allows for the prediction of people’s feelings, thoughts, impressions, and opinions. Sentiment analysis is regarded as one of the most active research areas in NLP. As a result, this tool has piqued the interest of marketing and business firms, government organizations, and society as a whole. Based on that, we propose a Tunisian model in this paper. A robustly optimized BERT approach was used to establish sentiment classification from the Tunisian corpus.

Copyright
© 2022 The Authors. Published by Atlantis Press International B.V.
Open Access
This is an open access article under the CC BY-NC license.

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Volume Title
Proceedings of the 2nd International Conference on Industry 4.0 and Artificial Intelligence (ICIAI 2021)
Series
Advances in Intelligent Systems Research
Publication Date
2 February 2022
ISBN
10.2991/aisr.k.220201.040
ISSN
1951-6851
DOI
10.2991/aisr.k.220201.040How to use a DOI?
Copyright
© 2022 The Authors. Published by Atlantis Press International B.V.
Open Access
This is an open access article under the CC BY-NC license.

Cite this article

risenwbib
TY  - CONF
AU  - Chaima Antit
AU  - Seifeddine Mechti
AU  - Rim Faiz
PY  - 2022
DA  - 2022/02/02
TI  - TunRoBERTa: A Tunisian Robustly Optimized BERT Approach Model for Sentiment Analysis
BT  - Proceedings of the 2nd International Conference on Industry 4.0 and Artificial Intelligence (ICIAI 2021)
PB  - Atlantis Press
SP  - 227
EP  - 231
SN  - 1951-6851
UR  - https://doi.org/10.2991/aisr.k.220201.040
DO  - 10.2991/aisr.k.220201.040
ID  - Antit2022
ER  -