mlsanityData to 7 Oct 2026

AraBERT and Farasa Segmentation Based Approach For Sarcasm and Sentiment Detection in Arabic Tweets

arXiv 2103.01679 ↗First posted 2 Mar 2021cs.CLcited by 4

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Abstract (arXiv)

This paper presents our strategy to tackle the EACL WANLP-2021 Shared Task 2: Sarcasm and Sentiment Detection. One of the subtasks aims at developing a system that identifies whether a given Arabic tweet is sarcastic in nature or not, while the other aims to identify the sentiment of the Arabic tweet. We approach the task in two steps. The first step involves pre processing the provided ArSarcasm-v2 dataset by performing insertions, deletions and segmentation operations on various parts of the text. The second step involves experimenting with multiple variants of two transformer based models, AraELECTRA and AraBERT. Our final approach was ranked seventh and fourth in the Sarcasm and Sentiment Detection subtasks respectively.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 7 of its 33 references that are in the map. First posted on arXiv 2 Mar 2021.

Builds on

7 of 33 references.

References · 7

  1. 2017Attention Is All You Needcited by 59,932
  2. 2013Distributed Representations of Words and Phrases and their Compositionalitycited by 3,462
  3. 2013Speech Recognition with Deep Recurrent Neural Networkscited by 666
  4. 2020AraBERT: Transformer-based Model for Arabic Language Understandingcited by 359
  5. 2020AraELECTRA: Pre-Training Text Discriminators for Arabic Language Understandingcited by 43
  6. 2016Modelling Context with User Embeddings for Sarcasm Detection in Social Mediacited by 26
  7. 2016Automatic Sarcasm Detection: A Surveycited by 21

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Citing papers per year: 2023: 2; 2024: 1; 2025: 1.

4 papers.