--- title: "From Arabic Sentiment Analysis to Sarcasm — Farha 2020" description: "Sarcasm undermines sentiment analysis because a speaker's real, usually negative, attitude is hidden behind positive-sounding wording." authors: ["Ibrahim Abu Farha", "Walid Magdy"] year: 2020 venue: "Proceedings of the 4th Workshop on Open-Source Arabic Corpora and Processing Tools, with a Shared Task on Offensive Language Detection" source: "https://aclanthology.org/2020.osact-1.5/" licence: "CC BY-NC 4.0 (ELRA, LREC proceedings)" licence_url: "https://creativecommons.org/licenses/by-nc/4.0/" summary_licence: "Elamur Cosmic License" canonical: "https://research.elamur.ai/free/en/papers/farha-2020-arabic-sentiment-analysis-sarcasm-detection/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/farha-2020-arabic-sentiment-analysis-sarcasm-detection/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [2020](https://md.research.elamur.ai/free/en/papers/2020/page/3/) / From Arabic Sentiment Analysis to Sarcasm Detection: The ArSarcasm Dataset # From Arabic Sentiment Analysis to Sarcasm Detection: The ArSarcasm Dataset **Authors:** [Ibrahim Abu Farha](https://md.research.elamur.ai/free/en/authors/ibrahim-abu-farha/), [Walid Magdy](https://md.research.elamur.ai/free/en/authors/walid-magdy/) · **Year:** 2020 · **Venue:** Proceedings of the 4th Workshop on Open-Source Arabic Corpora and Processing Tools, with a Shared Task on Offensive Language Detection · **Licence:** [CC BY-NC 4.0 (ELRA, LREC proceedings)](https://creativecommons.org/licenses/by-nc/4.0/) **Links:** [Publisher's page](https://aclanthology.org/2020.osact-1.5/) · [PDF](https://aclanthology.org/2020.osact-1.5.pdf) · [OpenAlex](https://openalex.org/W3088728183) **Citations:** 87 citations (openalex-snapshot · 2026-10-14) · [45](#cited-by) in our library, of the 140673 papers whose bibliographies we read ## Summary Sarcasm undermines sentiment analysis because a speaker's real, usually negative, attitude is hidden behind positive-sounding wording. Arabic had no usable public sarcasm corpus: earlier collections were never released or have largely vanished from Twitter. The authors fill this gap with ArSarcasm, built by relabelling tweets from existing Arabic sentiment datasets on the Figure-Eight crowdsourcing platform. Each tweet received three labels: sarcastic or not, a three-way sentiment label following SemEval guidelines, and a dialect tag. Comparing the new sentiment labels with the original ones shows strong annotator subjectivity, with labels shifting according to individual biases. The state-of-the-art analyser Mazajak performs clearly worse on sarcastic tweets, which suggests that such tools rely on surface polarity. A BiLSTM baseline reaches only 0.46 F1 on the sarcastic class. ArSarcasm matters as a freely downloadable Arabic sarcasm resource with dialect annotation. It also shows that detecting sarcasm requires context, world knowledge and cultural background beyond the text itself. ## Key points - ArSarcasm has 10,547 tweets, of which 1,682 (about 16%) are sarcastic. It is released for research at github.com/iabufarha/ArSarcasm. - The labels cover sarcasm (binary), sentiment (positive/negative/neutral) and dialect (Egyptian, Gulf, Levantine, Maghrebi, MSA). - Mazajak scored F1 0.43/0.44 on sarcastic tweets (new/original sentiment labels). On non-sarcastic tweets it scored 0.64/0.61. - The baseline is a BiLSTM (128 cells) with a dense layer, using Mazajak embeddings, the Adam optimiser and an 80/20 train/test split. - Baseline results on the sarcastic class were precision 0.62, recall 0.38 and F1 0.46. The low recall shows that much sarcasm goes undetected. *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 45 papers of our library cite it (of the 140673 whose bibliographies we read); 21 of them have a page here, 24 not yet. - [ARBERT & MARBERT: Deep Bidirectional Transformers for Arabic](https://md.research.elamur.ai/free/en/papers/abdul-2021-mageed-arbert-marbert-deep-bidirectional-transformers/) - [SarcasmDet at SemEval-2022 Task 6: Detecting Sarcasm using Pre-trained Transformers in English and Arabic Languages](https://md.research.elamur.ai/free/en/papers/abdullah-2022-sarcasmdet-semeval-2022-task-6/) - [Overview of the Arabic Sentiment Analysis 2021 Competition at KAUST](https://md.research.elamur.ai/free/en/papers/alamro-2021-overview-arabic-sentiment-analysis-2021/) - [Impact of emoji exclusion on the performance of Arabic sarcasm detection models](https://md.research.elamur.ai/free/en/papers/aleryani-2024-impact-emoji-exclusion-performance-arabic/) - [BARRAC: Adaptation of an English Aspect-based Sentiment Analysis Approach for Classification Tasks in Arabic Dialects](https://md.research.elamur.ai/free/en/papers/almutairi-2026-barrac-adaptation-english-aspect-based/) - [Content-Localization based Neural Machine Translation for Informal Dialectal Arabic: Spanish/French to Levantine/Gulf Arabic](https://md.research.elamur.ai/free/en/papers/alzamzami-2023-content-localization-based-neural-machine/) - [Content-Localization based System for Analyzing Sentiment and Hate Behaviors in Low-Resource Dialectal Arabic: English to Levantine and Gulf](https://md.research.elamur.ai/free/en/papers/alzamzami-2023-content-localization-based-system-analyzing/) - [OSN-MDAD: Machine Translation Dataset for Arabic Multi-Dialectal Conversations on Online Social Media](https://md.research.elamur.ai/free/en/papers/alzamzami-2023-osn-mdad-machine-translation-dataset/) - [AraCOVID19-SSD: Arabic COVID-19 Sentiment and Sarcasm Detection Dataset](https://md.research.elamur.ai/free/en/papers/ameur-2021-aracovid19-ssd-arabic-covid-19/) - [Whose Pragmatics? Cultural Grounding as a Bottleneck for Stereotype Detection in Egyptian Arabic Social Media](https://md.research.elamur.ai/free/en/papers/assem-2026-whose-pragmatics-cultural-grounding-as/) - [SemEval-2022 Task 6: iSarcasmEval, Intended Sarcasm Detection in English and Arabic](https://md.research.elamur.ai/free/en/papers/farha-2022-semeval-2022-task-6-isarcasmeval/) - [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/) - [On the importance of Data Scale in Pretraining Arabic Language Models](https://md.research.elamur.ai/free/en/papers/ghaddar-2024-importance-data-scale-pretraining-arabic/) - [SMASH at StanceEval 2024: Prompt Engineering LLMs for Arabic Stance Detection](https://md.research.elamur.ai/free/en/papers/hariri-2024-smash-stanceeval-2024-prompt-engineering/) - [The IDC System for Sentiment Classification and Sarcasm Detection in Arabic](https://md.research.elamur.ai/free/en/papers/israeli-2021-idc-system-sentiment-classification-sarcasm/) - [Estimating the Level of Dialectness Predicts Interannotator Agreement in Multi-dialect Arabic Datasets](https://md.research.elamur.ai/free/en/papers/keleg-2024-estimating-level-dialectness-predicts-interannotator/) - [Preprocessing Solutions for Detection of Sarcasm and Sentiment for Arabic](https://md.research.elamur.ai/free/en/papers/lichouri-2021-preprocessing-solutions-detection-sarcasm-sentiment/) - [Deep Multi-Task Model for Sarcasm Detection and Sentiment Analysis in Arabic Language](https://md.research.elamur.ai/free/en/papers/mahdaouy-2021-deep-multi-task-model-sarcasm/) - [AraT5: Text-to-Text Transformers for Arabic Language Generation](https://md.research.elamur.ai/free/en/papers/nagoudi-2022-arat5-text-text-transformers-arabic/) - [MarSan at SemEval-2022 Task 6: iSarcasm Detection via T5 and Sequence Learners](https://md.research.elamur.ai/free/en/papers/najafi-2022-marsan-semeval-2022-task-6/) - [Automatic Prompt Optimization with “Gradient Descent” and Beam Search](https://md.research.elamur.ai/free/en/papers/pryzant-2023-automatic-prompt-optimization-gradient-descent/) ## Cite The paper itself, not this page. BibTeX from the library's record of the work. ```bibtex @inproceedings{farha2020arabic, title = {{From Arabic Sentiment Analysis to Sarcasm Detection: The ArSarcasm Dataset}}, author = {Farha, Ibrahim Abu and Magdy, Walid}, booktitle = {Proceedings of the 4th Workshop on Open-Source Arabic Corpora and Processing Tools, with a Shared Task on Offensive Language Detection}, year = {2020}, url = {https://aclanthology.org/2020.osact-1.5/} } ``` - [← previous Free the Plural: Unrestricted Split-Antecedent Anaphora Resolution](https://md.research.elamur.ai/free/en/papers/yu-2020-free-plural-unrestricted-split-antecedent/) - [next → From Zero to Hero: On the Limitations of Zero-Shot Language Transfer with Multilingual Transformers](https://md.research.elamur.ai/free/en/papers/lauscher-2020-zero-hero-limitations-zero-shot/) - [↑ back to papers of 2020](https://md.research.elamur.ai/free/en/papers/2020/page/3/) ## References 45 references, 19 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet. 1. 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