# AraNet: A Deep Learning Toolkit for Arabic Social Media

arXiv: https://arxiv.org/abs/1912.13072 · HTML: https://mlsanity.com/paper/1912.13072/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Dec 2019.

## Built on by (most cited first, 14 of 14)

- [ORCA: A Challenging Benchmark for Arabic Language Understanding](https://mlsanity.com/paper/2212.10758/) (2022)
- [GPTAraEval: A Comprehensive Evaluation of ChatGPT on Arabic NLP](https://mlsanity.com/paper/2305.14976/) (2023)
- [NADI 2022: The Third Nuanced Arabic Dialect Identification Shared Task](https://mlsanity.com/paper/2210.09582/) (2022)
- [Emotion Detection for Misinformation: A Review](https://mlsanity.com/paper/2311.00671/) (2023)
- [TURJUMAN: A Public Toolkit for Neural Arabic Machine Translation](https://mlsanity.com/paper/2206.03933/) (2022)
- [Camelira: An Arabic Multi-Dialect Morphological Disambiguator](https://mlsanity.com/paper/2211.16807/) (2022)
- [Self-Training Pre-Trained Language Models for Zero- and Few-Shot Multi-Dialectal Arabic Sequence Labeling](https://mlsanity.com/paper/2101.04758/) (2021)
- [Octopus: A Multitask Model and Toolkit for Arabic Natural Language Generation](https://mlsanity.com/paper/2310.16127/) (2023)
- [Improving Social Meaning Detection with Pragmatic Masking and Surrogate Fine-Tuning](https://mlsanity.com/paper/2108.00356/) (2021)
- [The Skipped Beat: A Study of Sociopragmatic Understanding in LLMs for 64 Languages](https://mlsanity.com/paper/2310.14557/) (2023)
- [A Benchmark Study of Contrastive Learning for Arabic Social Meaning](https://mlsanity.com/paper/2210.12314/) (2022)
- [Contrastive Learning of Sociopragmatic Meaning in Social Media](https://mlsanity.com/paper/2203.07648/) (2022)
- [On the importance of Data Scale in Pretraining Arabic Language Models](https://mlsanity.com/paper/2401.07760/) (2024)
- [SinaTools: Open Source Toolkit for Arabic Natural Language Processing](https://mlsanity.com/paper/2411.01523/) (2024)

## Abstract (arXiv)

We describe AraNet, a collection of deep learning Arabic social media processing tools. Namely, we exploit an extensive host of publicly available and novel social media datasets to train bidirectional encoders from transformer models (BERT) to predict age, dialect, gender, emotion, irony, and sentiment. AraNet delivers state-of-the-art performance on a number of the cited tasks and competitively on others. In addition, AraNet has the advantage of being exclusively based on a deep learning framework and hence feature engineering free. To the best of our knowledge, AraNet is the first to performs predictions across such a wide range of tasks for Arabic NLP and thus meets a critical needs. We publicly release AraNet to accelerate research and facilitate comparisons across the different tasks.