@inproceedings{alghamdi-etal-2026-improving,
    title = "Improving on State-of-the-Art Models for Sentiment Analysis on Saudi-{E}nglish Code-Switching Text",
    author = "Alghamdi, Samaher  and
      Rayson, Paul  and
      Alotibi, Reem",
    booktitle = "Proceedings of the 2nd Workshop on {NLP} for Languages Using {A}rabic Script",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/manual-author-scripts/2026.abjadnlp-1.30/",
    pages = "218--228",
    abstract = "Inserting English words, phrases, or sentences while writing or speaking in the Saudi Arabic dialect has become a widespread phenomenon in Saudi society. This phenomenon is linguistically called code-switching. It remains unclear how current sentiment analysis methods perform on Saudi-English code-switching text. In this paper, we address this gap by conducting the first sentiment analysis study on Saudi-English code-switching text. We present the first Saudi-English Sentiment Analysis Code Switching Dataset (SESA-CSD) and establish baseline results on this dataset. By evaluating multiple state-of-the-art small language models, we achieve improvements over the baseline of 3{\%} to 11{\%} in both accuracy and macro-F1. Among all small language models, XLM-RoBERTa achieved the highest performance,with an accuracy of 95.50{\%} and a macro-F1 of 95.53{\%}. Our findings indicate that multilingual and Arabic small language models, such as XLM-RoBERTa, GigaBERT, and SaudiBERT, consistently outperform bilingual Arabic-English large language models, such as Fanar and ALLaM, across zero-shot and multiple few-shot settings."
}