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

arXiv: https://arxiv.org/abs/2103.01679 · HTML: https://mlsanity.com/paper/2103.01679/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 33 references that are in the map.
- First posted on arXiv 2 Mar 2021.

## Builds on (most cited first, 7 of 7)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Speech Recognition with Deep Recurrent Neural Networks](https://mlsanity.com/paper/1303.5778/) (2013)
- [AraBERT: Transformer-based Model for Arabic Language Understanding](https://mlsanity.com/paper/2003.00104/) (2020)
- [AraELECTRA: Pre-Training Text Discriminators for Arabic Language Understanding](https://mlsanity.com/paper/2012.15516/) (2020)
- [Modelling Context with User Embeddings for Sarcasm Detection in Social Media](https://mlsanity.com/paper/1607.00976/) (2016)
- [Automatic Sarcasm Detection: A Survey](https://mlsanity.com/paper/1602.03426/) (2016)

## Built on by (most cited first, 4 of 4)

- [SAIDS: A Novel Approach for Sentiment Analysis Informed of Dialect and Sarcasm](https://mlsanity.com/paper/2301.02521/) (2023)
- [Arabic Sentiment Analysis with Noisy Deep Explainable Model](https://mlsanity.com/paper/2309.13731/) (2023)
- [Ensembling Multilingual Transformers for Robust Sentiment Analysis of Tweets](https://mlsanity.com/paper/2509.24080/) (2025)
- [Ensemble Language Models for Multilingual Sentiment Analysis](https://mlsanity.com/paper/2403.06060/) (2024)

## 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.