Aurat March Sentiment Analysis (Urdu & English)
Fine-tuned microsoft/Multilingual-MiniLM-L12-H384 for three-class sentiment classification of Pakistani news coverage of the Aurat March feminist movement.
Labels: Oppositional · Supportive · Neutral
Developed as part of the Master's Thesis "Compact Transformer-Based Sentiment Analysis of Aurat March Media Coverage in Urdu and English" at HSE University, Nizhny Novgorod.
Usage
from transformers import pipeline
classifier = pipeline(
"sentiment-analysis",
model="aakashMeghwar01/aurat-march-sentiment-minilm"
)
# English example
classifier("The Aurat March is an important movement for women's rights in Pakistan")
# → [{'label': 'Supportive', 'score': 0.87}]
# Urdu example
classifier("عورت مارچ کے نعرے اسلامی اقدار کے خلاف ہیں")
# → [{'label': 'Oppositional', 'score': 0.79}]
Evaluation
| Class | Precision | Recall | F1 | Support |
|---|---|---|---|---|
| Oppositional | 0.89 | 0.81 | 0.85 | 21 |
| Supportive | 0.83 | 0.88 | 0.86 | 17 |
| Neutral | 0.67 | 0.75 | 0.71 | 8 |
| Weighted avg | 0.83 | 0.83 | 0.83 | 46 |
Training
- Base model:
microsoft/Multilingual-MiniLM-L12-H384 - Training corpus: 470 annotated Urdu and English news articles (Aurat March coverage 2018–2023)
- Class weighting: WeightedTrainer with inverse-frequency weights to handle class imbalance
- Epochs: 10 · Batch size: 16 · Learning rate: 2e-5
- Hardware: Google Colab T4 GPU
Labels
| ID | Label | Description |
|---|---|---|
| 0 | Oppositional | Article frames the Aurat March negatively |
| 1 | Supportive | Article frames the Aurat March positively |
| 2 | Neutral | Procedural/balanced reporting |
Citation
Meghwar, A. (2025). Compact Transformer-Based Sentiment Analysis of Aurat March
Media Coverage in Urdu and English. MA Thesis, HSE University Nizhny Novgorod.
Model: https://huggingface.co/aakashMeghwar01/aurat-march-sentiment-minilm
Contact
Aakash Meghwar · aakashmeghwar01@gmail.com · LinkedIn It utilizes a Compact Transformer (MiniLM-L12-v2) architecture, fine-tuned specifically to navigate the nuanced socio-political discourse of the Aurat March in Pakistan. The model is optimized for high performance on consumer-grade hardware (T4 GPUs), adhering to the "Green AI" philosophy of accessible NLP for low-resource languages.
🚀 About the Author
I am Aakash Meghwar, a Computational Linguist specializing in South Asian languages.
- 🎓 M.S. in Applied Linguistics & Text Analytics (HSE, Russia - Graduating June 2026)
- 🎓 B.S. in English Language & Literature (NUML, Islamabad)
- 💡 Founder: Text Tech Solutions
- ✍️ Published Researcher: Author of "Compact Transformer Models for Classical Urdu Poetry" (Corporum Journal).
🤝 Open for Collaboration & PhD Opportunities
I am actively seeking PhD opportunities and Research Collaborations in the following areas:
- Low-Resource NLP: Developing efficient models for Sindhi, Urdu, and Siraiki.
- Literary Informatics: Computational stylistics and affective registers in classical poetry.
- SindhiLM Evolution: I am currently developing SindhiLM-v2 (featuring an improved Sindhi-BPE tokenizer and knowledge distillation).
Are you looking for a researcher to join your NLP lab or organization? I am open to discussing projects involving Model Compression, Cross-Lingual Transfer, and South Asian Language Tech.
📩 Contact & Links
- Email: aakashmeghwar01@gmail.com
- LinkedIn: Aakash Meghwar
- Projects: SindhiLM | Urdu Poetry Research
How to use
from transformers import pipeline
classifier = pipeline("sentiment-analysis", model="aakashMeghwar01/aurat-march-sentiment-minilm")
# Example Urdu input
classifier("عورت مارچ خواتین کے حقوق کی ایک اہم تحریک ہے")