# ASAD: A Twitter-based Benchmark Arabic Sentiment Analysis Dataset

arXiv: https://arxiv.org/abs/2011.00578 · HTML: https://mlsanity.com/paper/2011.00578/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Nov 2020.

## Built on by (most cited first, 7 of 7)

- [TwHIN-BERT: A Socially-Enriched Pre-trained Language Model for Multilingual Tweet Representations at Twitter](https://mlsanity.com/paper/2209.07562/) (2022)
- [BESSTIE: A Benchmark for Sentiment and Sarcasm Classification for Varieties of English](https://mlsanity.com/paper/2412.04726/) (2024)
- [Overview of the Arabic Sentiment Analysis 2021 Competition at KAUST](https://mlsanity.com/paper/2109.14456/) (2021)
- [Arabic Tweet Act: A Weighted Ensemble Pre-Trained Transformer Model for Classifying Arabic Speech Acts on Twitter](https://mlsanity.com/paper/2401.17373/) (2024)
- [AlcLaM: Arabic Dialectal Language Model](https://mlsanity.com/paper/2407.13097/) (2024)
- [GLARE: Google Apps Arabic Reviews Dataset](https://mlsanity.com/paper/2412.15259/) (2024)
- [Audience Engagement with Arabic Women's Social Empowerment and Wellbeing: A Decadal Corpus](https://mlsanity.com/paper/2605.22204/) (2026)

## Abstract (arXiv)

This paper provides a detailed description of a new Twitter-based benchmark dataset for Arabic Sentiment Analysis (ASAD), which is launched in a competition3, sponsored by KAUST for awarding 10000 USD, 5000 USD and 2000 USD to the first, second and third place winners, respectively. Compared to other publicly released Arabic datasets, ASAD is a large, high-quality annotated dataset(including 95K tweets), with three-class sentiment labels (positive, negative and neutral). We presents the details of the data collection process and annotation process. In addition, we implement several baseline models for the competition task and report the results as a reference for the participants to the competition.