Update README.md

README.md

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-# vaovao_malagasy_sentiment_corpus
-
 # Dataset Card for Vaovao Malagasy Sentiment Corpus (VMSC)
 
 ## Dataset Summary
 
-The **Vaovao Malagasy Sentiment Corpus (VMSC)** is the first publicly available, manually annotated sentiment dataset for the Malagasy language. It contains **5,041 sentences** from news articles published between 2022 and 2023, each labeled with binary sentiment (positive or negative).
+The **Vaovao Malagasy Sentiment Corpus (VMSC)** is the first publicly available, manually annotated sentiment analysis dataset for the Malagasy language (`mg`). It contains **5,041 sentences** extracted from news articles (*vaovao*) published between 2022 and 2023.
 
-This dataset was created by native speakers using a high-quality annotation protocol, achieving **Cohen’s Kappa = 0.96**. VMSC enables research in sentiment analysis and other downstream NLP tasks for Malagasy, a low-resource language spoken by over 25 million people.
+Each sentence is labeled with binary sentiment (**Positive** or **Negative**). The dataset was created to address the scarcity of resources for Malagasy NLP and benchmarks. It features high-quality annotations performed by native speakers, achieving an inter-annotator agreement of **Cohen’s Kappa = 0.96**.
 
----
+This dataset is the official corpus presented in the paper published in *Language Resources and Evaluation* (Springer, 2025).
 
 ## Supported Tasks
 
-- Sentiment Analysis (binary classification)
-- Text Classification
-- Sentence-level Language Modeling
-- Benchmarking for Low-resource NLP
-
----
+- **Sentiment Analysis:** Binary classification (Positive vs. Negative).
+- **Transfer Learning:** Benchmarking pre-trained models (e.g., XLM-R, mBERT) on Malagasy.
+- **Low-Resource NLP:** Serving as a gold-standard evaluation set for low-resource language studies.
 
 ## Languages
 
-- **Language**: Malagasy (`mg`)
-- **Variant**: Standard Malagasy
+- **Language:** Malagasy (`mg`)
+- **Variant:** Standard Malagasy (official dialect used in media and government).
 
----
 
+## Dataset Structure
+
+### Data Instances
 
-## How to Use
+An example of a data point looks like this:
+
+```json
+{
+  "text": "Tena nety ny vokatra azo tamin'ny tetikasa.",
+  "label": 1
+}
+```
+### Data Fields
+- text: The news sentence (string).
+- label: The sentiment label (integer).
+  * 0: Negative
+  * 1: Positive
+    
+### Data Splits
+The dataset contains a total of 5,041 sentences.
+
+### Dataset Creation
+* Source: Malagasy news articles published between 2022 and 2023.
+* Annotation: Manually annotated by native speakers.
+* Quality Control: Rigorous annotation protocol resulting in a Cohen’s Kappa score of 0.96.
+  
+### How to Use
+You can load this dataset directly using the Hugging Face datasets library:
 
 ```python
 from datasets import load_dataset
 
-dataset = load_dataset('Lo-Renz-O/vaovao_malagasy_sentiment_corpus')
-print(dataset['train'][0])
+# Load the dataset
+dataset = load_dataset("Lo-Renz-O/vaovao_malagasy_sentiment_corpus")
+
+# View the first training example
+print(dataset['train'][0])
+```
+
+### Citation
+
+```bibtex
+@article{Mamelona2025VMSC,
+  author = {Lorenzo Mamelona, Sitraka Ny Aina Raharivelo, Andriamanarivo Tatiana Miarintsoa, Bright Bediako-Kyeremeh & Benjamin Kwapong Osibo}, 
+  title = {Towards robust Malagasy NLP: a novel corpus and evaluation framework for sentiment analysis},
+  journal = {Language Resources and Evaluation},
+  year = {2026},
+  publisher = {Springer},
+  doi = {10.1007/s10579-025-09900-w},
+  url = {https://link.springer.com/article/10.1007/s10579-025-09900-w}
+}
+```