# TUNIZI: a Tunisian Arabizi sentiment analysis Dataset

arXiv: https://arxiv.org/abs/2004.14303 · HTML: https://mlsanity.com/paper/2004.14303/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Apr 2020.

## Built on by (most cited first, 11 of 11)

- [ORCA: A Challenging Benchmark for Arabic Language Understanding](https://mlsanity.com/paper/2212.10758/) (2022)
- [NADI 2022: The Third Nuanced Arabic Dialect Identification Shared Task](https://mlsanity.com/paper/2210.09582/) (2022)
- [State of NLP in Kenya: A Survey](https://mlsanity.com/paper/2410.09948/) (2024)
- [A Multilingual African Embedding for FAQ Chatbots](https://mlsanity.com/paper/2103.09185/) (2021)
- [A Benchmark Study of Contrastive Learning for Arabic Social Meaning](https://mlsanity.com/paper/2210.12314/) (2022)
- [TEET! Tunisian Dataset for Toxic Speech Detection](https://mlsanity.com/paper/2110.05287/) (2021)
- [TunBERT: Pretrained Contextualized Text Representation for Tunisian Dialect](https://mlsanity.com/paper/2111.13138/) (2021)
- [Misinformation detection in Luganda-English code-mixed social media text](https://mlsanity.com/paper/2104.00124/) (2021)
- [From Local to Global: Navigating Linguistic Diversity in the African Context](https://mlsanity.com/paper/2305.01427/) (2023)
- [Homograph Attacks on Maghreb Sentiment Analyzers](https://mlsanity.com/paper/2402.03171/) (2024)
- [Decoding Multilingual Topic Dynamics and Trend Identification through ARIMA Time Series Analysis on Social Networks: A Novel Data Translation Framework Enhanced by LDA/HDP Models](https://mlsanity.com/paper/2403.15445/) (2024)

## Abstract (arXiv)

On social media, Arabic people tend to express themselves in their own local dialects. More particularly, Tunisians use the informal way called "Tunisian Arabizi". Analytical studies seek to explore and recognize online opinions aiming to exploit them for planning and prediction purposes such as measuring the customer satisfaction and establishing sales and marketing strategies. However, analytical studies based on Deep Learning are data hungry. On the other hand, African languages and dialects are considered low resource languages. For instance, to the best of our knowledge, no annotated Tunisian Arabizi dataset exists. In this paper, we introduce TUNIZI a sentiment analysis Tunisian Arabizi Dataset, collected from social networks, preprocessed for analytical studies and annotated manually by Tunisian native speakers.