@inproceedings{nwesri-etal-2026-university,
title = "University of Tripoli at {A}ra{S}ent{E}val: Fine-Tuning {MARBERT}v2 and {CAMELBERT} for Multi-Dialect {A}rabic Sentiment Analysis",
author = "Nwesri, Abdusalam F. Ahmad and
Sharif, Amani Bahlul and
Hmeid, Sarah Farag S.",
editor = "Al-Khalifa, Hend and
El-Haj, Mo and
Ezzini, Saad",
booktitle = "The 7th Workshop on Open-Source {A}rabic Corpora and Processing Tools ({OSACT}7) with 5 Shared Tasks",
month = may,
year = "2026",
address = "Palma, Mallorca (Spain)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.osact-1.42/",
doi = "10.63317/3zunt9qnt2i4",
pages = "296--301",
abstract = "This paper presents our contribution to the AraSentEval 2026 shared task, specifically for Subtask 1: Arabic Dialect Sentiment Analysis, hosted at the OSACT7 workshop during LREC 2026. The task focuses on classifying the sentiment (positive, negative, neutral) of text written in four major Arabic dialects: Moroccan, Egyptian, Jordanian, and Saudi. We addressed this by fine-tuning several pre-trained language models, including MARBERTv2 and CAMELBERT, on the provided Multi-Dialect-Sent (MDS-3) dataset. Our best-performing system MARBERTv2, achieved a Macro F1-score of 84.29{\%} on the official test set, securing fourth place among 13 participating teams. Our findings underscore the value of leveraging large pre-trained models tailored to dialectal Arabic for improved sentiment classification in this under-resourced domain."
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<abstract>This paper presents our contribution to the AraSentEval 2026 shared task, specifically for Subtask 1: Arabic Dialect Sentiment Analysis, hosted at the OSACT7 workshop during LREC 2026. The task focuses on classifying the sentiment (positive, negative, neutral) of text written in four major Arabic dialects: Moroccan, Egyptian, Jordanian, and Saudi. We addressed this by fine-tuning several pre-trained language models, including MARBERTv2 and CAMELBERT, on the provided Multi-Dialect-Sent (MDS-3) dataset. Our best-performing system MARBERTv2, achieved a Macro F1-score of 84.29% on the official test set, securing fourth place among 13 participating teams. Our findings underscore the value of leveraging large pre-trained models tailored to dialectal Arabic for improved sentiment classification in this under-resourced domain.</abstract>
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%0 Conference Proceedings
%T University of Tripoli at AraSentEval: Fine-Tuning MARBERTv2 and CAMELBERT for Multi-Dialect Arabic Sentiment Analysis
%A Nwesri, Abdusalam F. Ahmad
%A Sharif, Amani Bahlul
%A Hmeid, Sarah Farag S.
%Y Al-Khalifa, Hend
%Y El-Haj, Mo
%Y Ezzini, Saad
%S The 7th Workshop on Open-Source Arabic Corpora and Processing Tools (OSACT7) with 5 Shared Tasks
%D 2026
%8 May
%I Association for Computational Linguistics
%C Palma, Mallorca (Spain)
%F nwesri-etal-2026-university
%X This paper presents our contribution to the AraSentEval 2026 shared task, specifically for Subtask 1: Arabic Dialect Sentiment Analysis, hosted at the OSACT7 workshop during LREC 2026. The task focuses on classifying the sentiment (positive, negative, neutral) of text written in four major Arabic dialects: Moroccan, Egyptian, Jordanian, and Saudi. We addressed this by fine-tuning several pre-trained language models, including MARBERTv2 and CAMELBERT, on the provided Multi-Dialect-Sent (MDS-3) dataset. Our best-performing system MARBERTv2, achieved a Macro F1-score of 84.29% on the official test set, securing fourth place among 13 participating teams. Our findings underscore the value of leveraging large pre-trained models tailored to dialectal Arabic for improved sentiment classification in this under-resourced domain.
%R 10.63317/3zunt9qnt2i4
%U https://aclanthology.org/2026.osact-1.42/
%U https://doi.org/10.63317/3zunt9qnt2i4
%P 296-301
Markdown (Informal)
[University of Tripoli at AraSentEval: Fine-Tuning MARBERTv2 and CAMELBERT for Multi-Dialect Arabic Sentiment Analysis](https://aclanthology.org/2026.osact-1.42/) (Nwesri et al., OSACT 2026)
ACL