Update README.md

README.md

@@ -23,4 +23,142 @@ configs:
     path: data/train-*
   - split: test
     path: data/test-*
+license: lgpl-3.0
+task_categories:
+- text-classification
+language:
+- ar
+tags:
+- arabic
+- tunisian
+- sentiment_analysis
+pretty_name: Tunisian Sentiment Analysis Corpus (TSAC)
+size_categories:
+- 10K<n<100K
 ---
+
+# Tunisian Sentiment Analysis Corpus (TSAC)
+
+The **Tunisian Sentiment Analysis Corpus (TSAC)** is a collection of approximately **17,000 Tunisian Arabic user comments** manually annotated for **sentiment polarity** (positive or negative).  
+It was collected from Facebook comments written on the official pages of Tunisian radio and TV stations between **January 2015 and June 2016**.
+
+This cleaned and Hugging Face–ready version of TSAC provides train/test splits in a simple format compatible with any modern NLP framework.
+
+---
+
+## Dataset Details
+
+### Dataset Description
+
+- **Name:** Tunisian Sentiment Analysis Corpus (TSAC)
+- **Curated by:** Salima Medhaffar, Fethi Bougares, Yannick Estève, and Lamia Hadrich-Belguith  
+- **Language:** Tunisian Arabic (Arabic script)
+- **License:** Apache License 2.0
+- **Original Repository:** [https://github.com/fbougares/TSAC](https://github.com/fbougares/TSAC)
+- **Hugging Face Maintainer:** [tunis-ai](https://huggingface.co/tunis-ai)
+
+### Dataset Sources
+
+- **Data collected from:** Official Facebook pages of  
+  - Mosaique FM  
+  - Jawhara FM  
+  - Shems FM  
+  - Hiwar Ettounsi TV  
+  - Nessma TV  
+- **Timeframe:** January 2015 – June 2016
+- **Paper:** [Medhaffar et al., 2017 — *Sentiment Analysis of Tunisian Dialects: Linguistic Resources and Experiments*](https://aclanthology.org/W17-1307/)
+
+---
+
+## Uses
+
+### Direct Use
+
+The dataset is suitable for:
+- Sentiment analysis in Tunisian Arabic
+- Dialectal Arabic language modeling
+- Evaluation of cross-dialectal or multilingual sentiment models
+
+### Out-of-Scope Use
+
+- Not suitable for general Modern Standard Arabic (MSA) tasks.
+- Not recommended for topic classification or sarcasm detection without adaptation.
+
+---
+
+## Dataset Structure
+
+### Data Fields
+
+| Field | Type | Description |
+|-------|------|-------------|
+| `sentence` | string | User comment written in Tunisian Arabic |
+| `label` | int | Sentiment label (1 = positive, 0 = negative) |
+
+### Splits
+
+| Split | # Examples |
+|--------|-------------|
+| Train | 13,669 |
+| Test  | 3,400 |
+
+Splits were created using a stratified partition to maintain class balance.
+
+---
+
+## Dataset Creation
+
+### Curation Rationale
+
+The dataset was built to support research in sentiment analysis for **Tunisian Arabic**, a dialect that differs significantly from Modern Standard Arabic and lacks large-scale annotated resources.
+
+### Data Collection and Processing
+
+Comments were collected from Facebook public pages using web scraping tools, manually filtered for relevance, and annotated by native Tunisian speakers into two polarity classes: **positive** and **negative**.
+
+Preprocessing steps include:
+- Removing URLs, emojis, and metadata
+- Normalizing Arabic characters
+- Deduplicating sentences
+
+### Source Data Producers
+
+Public Facebook users posting on the mentioned Tunisian media pages between 2015 and 2016.
+
+### Annotations
+
+- **Annotation Type:** Binary sentiment classification (positive/negative)
+- **Annotators:** Native Tunisian Arabic speakers
+- **Validation:** Manual cross-checking and agreement verification by linguists
+
+### Personal and Sensitive Information
+
+Comments are publicly available and were anonymized by removing any identifiable information (e.g., usernames, mentions).
+
+---
+
+## Bias, Risks, and Limitations
+
+The dataset reflects opinions expressed on public Facebook pages and may include demographic, temporal, or topical biases.  
+It should not be used to infer general population sentiment or to train systems that make sensitive decisions about individuals.
+
+---
+
+## Citation
+
+If you use this dataset, please cite the following paper:
+
+**BibTeX:**
+```bibtex
+@inproceedings{medhaffar-etal-2017-sentiment,
+    title = "Sentiment Analysis of {T}unisian Dialects: Linguistic Resources and Experiments",
+    author = "Medhaffar, Salima  and Bougares, Fethi  and Estève, Yannick  and Hadrich-Belguith, Lamia",
+    booktitle = "Proceedings of the Third Arabic Natural Language Processing Workshop",
+    month = apr,
+    year = "2017",
+    address = "Valencia, Spain",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/W17-1307/",
+    pages = "55--61",
+    doi = "10.18653/v1/W17-1307"
+}