--- title: "Sarcasm and Sentiment Detection In Arabic — Abuzayed 2021" description: "The paper describes a system for the shared task on Arabic sarcasm detection (Subtask 1) and three-way sentiment analysis (Subtask." authors: ["Abeer Abuzayed", "Hend Al-Khalifa"] year: 2021 venue: "Proceedings of the Sixth Arabic Natural Language Processing Workshop" source: "https://aclanthology.org/2021.wanlp-1.38/" licence: "CC BY 4.0 (ACL Anthology)" licence_url: "https://creativecommons.org/licenses/by/4.0/" summary_licence: "Elamur Cosmic License" canonical: "https://research.elamur.ai/free/en/papers/abuzayed-2021-sarcasm-sentiment-detection-arabic-tweets/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/abuzayed-2021-sarcasm-sentiment-detection-arabic-tweets/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [2021](https://md.research.elamur.ai/free/en/papers/2021/page/4/) / Sarcasm and Sentiment Detection In Arabic Tweets Using BERT-based Models and Data Augmentation # Sarcasm and Sentiment Detection In Arabic Tweets Using BERT-based Models and Data Augmentation **Authors:** Abeer Abuzayed, [Hend Al-Khalifa](https://md.research.elamur.ai/free/en/authors/hend-al-khalifa/) · **Year:** 2021 · **Venue:** Proceedings of the Sixth Arabic Natural Language Processing Workshop · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/) **Links:** [Publisher's page](https://aclanthology.org/2021.wanlp-1.38/) · [PDF](https://aclanthology.org/2021.wanlp-1.38.pdf) · [OpenAlex](https://openalex.org/W3156324355) **Citations:** 25 citations (openalex-snapshot · 2026-10-14) · [11](#cited-by) in our library, of the 140884 papers whose bibliographies we read ## Summary The paper describes a system for the shared task on Arabic sarcasm detection (Subtask 1) and three-way sentiment analysis (Subtask 2) on the ArSarcasm-v2 tweet dataset. The training data is small and skewed: only about one tweet in six is sarcastic, and positive tweets are similarly rare. After trying classical classifiers on tf-idf features and several deep networks, the authors fine-tuned seven Arabic and multilingual BERT-based models. MARBERT and QARiB performed best. To address the imbalance, they added extra training data from the ASAD sentiment corpus. Because almost all sarcastic tweets in the training set were also negative, they relabelled ASAD negative tweets as sarcastic and positive ones as not sarcastic. On the validation split, this raised F-scores by roughly 15 points for both subtasks. On the official test set, however, the gains largely disappeared, which suggests overfitting or a mismatch between the proxy labels and true sarcasm. An error analysis points to annotation noise, missing context, literal mentions of the word "sarcasm", and unprocessed emojis as the main failure sources. ## Key points - ArSarcasm-v2 has 12,548 training tweets and 3,000 test tweets. Sarcasm labels split 2,168 TRUE against 10,380 FALSE. Sentiment labels split NEU 5,747, NEG 4,621 and POS 2,180. - Seven models were compared under identical settings: MARBERT, ArBERT, QARiB, AraBERTv02, GigaBERTv3, Arabic BERT and mBERT. All used 5 epochs, a learning rate of 2e-06, batch size 32 and an 80/20 train/validation split. - On the original data, MARBERT reached 0.65 F1 on the sarcastic class, QARiB 0.60 and mBERT only 0.41. For sentiment (F-PN), QARiB scored 0.73 and MARBERT 0.71, but QARiB overfit more. - Augmentation drew on 29,924 retrieved ASAD tweets and added 4,739 TRUE and 4,930 FALSE examples, giving 22,217 training tweets. On validation, MARBERT rose to 0.80 F1-sarcastic (QARiB to 0.75) and to 0.86 F-PN. - MARBERT's official test scores were much lower than its validation scores: 0.52 F1-sarcastic and 0.71 F-PN. ## Interesting ideas - Using a strong co-occurrence between labels (89% of sarcastic tweets are negative) to borrow examples from a neighbouring task's corpus is a cheap way to rebalance rare classes, though the test drop shows the proxy is noisy. — For Mova (learn): Silver irony or rare-frame examples could be harvested from correlated annotations, but only if they pass the same held-out negative-control check that exposed this paper's gap. - The large gap between validation and test scores is itself the key finding, because improvements measured on a split drawn from augmented or same-distribution data can be illusory. — For Mova (learn): Any LLM-teacher or silver-data gain should be confirmed on an untouched, differently sourced test set before it is accepted. - The error analysis found tweets that merely mention the word for sarcasm without being sarcastic, which separates talking about a phenomenon from performing it. — For Mova (frames): The irony module should treat words like "sarcasm" or "ironic" as topic mentions that the tree and frames must confirm, never as direct triggers. - Emojis that carry the opposite polarity to the literal text misled the classifier, yet such a clash between text and emoji is exactly the incongruity that often signals irony. — For Mova (frames): A polarity clash between an emoji and the evaluated frame could be logged as an explainable irony cue rather than averaged away. - The preprocessing splits hashtags into words by replacing underscores, collapses elongated characters, and normalises letter variants, turning noisy social tokens into analysable words. — For Mova (tokens): The en tokenizer could split hashtags such as #worst_day_ever and collapse repeated letters ("sooooo") before tagging, recording the original form for explanation. *Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.* Fields: [Linguistics & languages](https://md.research.elamur.ai/free/en/shelves/linguistics/) ## Cited by Cited by 11 papers of our library cite it (of the 140884 whose bibliographies we read); 4 of them have a page here, 7 not yet. - [Spam and Sentiment Detection in Arabic Tweets Using MARBERT Model](https://md.research.elamur.ai/free/en/papers/alotaibi-2026-spam-sentiment-detection-arabic-tweets/) - [Overview of the WANLP 2021 Shared Task on Sarcasm and Sentiment Detection in Arabic](https://md.research.elamur.ai/free/en/papers/farha-2021-overview-wanlp-2021-shared-task/) - [Sarcasm Detection is Way Too Easy! An Empirical Comparison of Human and Machine Sarcasm Detection](https://md.research.elamur.ai/free/en/papers/farha-2022-sarcasm-detection-is-way-too/) - [Data Augmentation using Transformers and Similarity Measures for Improving Arabic Text Classification](https://md.research.elamur.ai/free/en/papers/refai-2022-data-augmentation-using-transformers-similarity/) ## Cite The paper itself, not this page. BibTeX from the library's record of the work. ```bibtex @inproceedings{abuzayed2021sarcasm, title = {{Sarcasm and Sentiment Detection In Arabic Tweets Using BERT-based Models and Data Augmentation}}, author = {Abuzayed, Abeer and Al-Khalifa, Hend}, booktitle = {Proceedings of the Sixth Arabic Natural Language Processing Workshop}, year = {2021}, url = {https://aclanthology.org/2021.wanlp-1.38/} } ``` - [← previous Sarcasm and Sentiment Detection in Arabic language A Hybrid Approach Combining Embeddings and Rule-based Features](https://md.research.elamur.ai/free/en/papers/gaanoun-2021-sarcasm-sentiment-detection-arabic-language/) - [next → Sarcasm and Sentiment Detection in Arabic: investigating the interest of character-level features](https://md.research.elamur.ai/free/en/papers/ghoul-2021-sarcasm-sentiment-detection-arabic-investigating/) - [↑ back to papers of 2021](https://md.research.elamur.ai/free/en/papers/2021/page/4/) ## References 3 references, 3 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet. 1. Abbes, I., Zaghouani, W., El-Hardlo, O. & Ashour, 2020. DAICT: A Dialectal Arabic Irony Corpus Extracted from Twitter. LREC 2020. [find](https://scholar.google.com/scholar?q=Abbes%2C+I%2E%2C+Zaghouani%2C+W%2E%2C+El%2DHardlo%2C+O%2E+%26+Ashour%2C+2020%2E+DAICT%3A+A+Dialectal+Arabic+Irony+Corpus+Extracted+from+Twitter%2E+LREC+2020%2E) [our card](https://md.research.elamur.ai/free/en/papers/abbes-2020-daict-dialectal-arabic-irony-corpus/) 2. NEG Abdul-Mageed, M., Elmadany, A. and Nagoudi, E.M.B., 2020. ARBERT & MARBERT: Deep Bidirectional Transformers for Arabic. arXiv preprint arXiv:2101.01785. https://t.co/LNjehLMxsQ " 5 "# يسيم اوتاه مكلوقي سورق مهدلجيب 😎 رامينو زيراوسو مايا ثلاث يف للاهلا دلجام لثم 😎 ياج حيار هنولشرب _ يلهلاا # https://t.co…" 6 " كيتب يسفن يقلاا ام للهاشنا موي مج دعب كوت 😂😂" NEG [↗](https://arxiv.org/abs/2101.01785) [our card](https://md.research.elamur.ai/free/en/papers/abdul-2021-mageed-arbert-marbert-deep-bidirectional-transformers/) 3. NEG Abu Farha, I., Zaghouani, W. and Magdy, W., 2021. Overview of the WANLP 2021 Shared Task on Sarcasm and Sentiment Detection in Arabic. Proceedings of the Sixth Arabic Natural Language Processing Workshop. POS *Table 8: Examples of mis-clasffied tweets for sentiment class.* Alharbi, B., Alamro, H., Alshehri, M., Khayyat, Z., Kalkatawi, M., Jaber, I.I. and Zhang, X., 2020. ASAD: A Twitter-based Benchmark Arabic Sentiment Analysis Dataset. arXiv preprint arXiv:2011.00578. [↗](https://arxiv.org/abs/2011.00578) [our card](https://md.research.elamur.ai/free/en/papers/alharbi-2020-asad-twitter-based-benchmark-arabic/)