---
language: ne
tags:
- sentiment-analysis
- nepali
- nepse
- stock-market
- finance
- bilstm
- cnn
- keras
license: mit
datasets:
- custom
metrics:
- accuracy
model-index:
- name: nepse-sentiment-analyzer
  results:
  - task:
      type: text-classification
      name: Sentiment Analysis
    dataset:
      type: custom
      name: NEPSE Financial News (Nepali)
    metrics:
    - type: accuracy
      value: 0.9766
      name: Training Accuracy
---

# NEPSE Sentiment Analyzer 📈

A BiLSTM-CNN hybrid neural network for sentiment analysis of Nepali financial text, specifically designed for Nepal Stock Exchange (NEPSE) related content.

## Model Description

This model performs 5-class sentiment classification on Nepali text:
- **Very Negative** (0): बहुत नकारात्मक
- **Negative** (1): नकारात्मक  
- **Neutral** (2): तटस्थ
- **Positive** (3): सकारात्मक
- **Very Positive** (4): बहुत सकारात्मक

## Architecture

- **Model Type**: BiLSTM-CNN Hybrid
- **Parameters**: 126,333 trainable parameters
- **Framework**: TensorFlow/Keras
- **Input**: Nepali text (max 100 tokens)
- **Output**: 5-class probability distribution

### Architecture Details:
```
Input Layer (100 tokens) 
    ↓
Embedding Layer (100 dims)
    ↓
BiLSTM Layer (64 units)
    ↓
CNN Layer (128 filters, kernel=3)
    ↓
Global Max Pooling
    ↓
Dense Layer (32 units, ReLU)
    ↓
Output Layer (5 units, Softmax)
```

## Training Data

- **Dataset Size**: 2,986 samples
- **Language**: Nepali (Devanagari script)
- **Domain**: Financial/Stock Market
- **Distribution**: Balanced across 5 sentiment classes
- **Augmentation**: Synonym replacement and paraphrasing

## Performance

| Metric | Score |
|--------|-------|
| Training Accuracy | 97.66% |
| Validation Accuracy | ~95% |
| Parameters | 126,333 |
| Model Size | 1.5 MB |

## Usage

```python
import tensorflow as tf
from tensorflow.keras.models import load_model
import numpy as np

# Load the model
model = load_model('best_model.keras')

# Prepare text (example)
def preprocess_text(text):
    # Add your preprocessing logic here
    # Tokenization, padding, etc.
    return processed_text

# Predict sentiment
text = "शेयर बजारमा राम्रो वृद्धि भएको छ"
processed = preprocess_text(text)
prediction = model.predict(processed)
sentiment_class = np.argmax(prediction)

# Classes: 0=Very Negative, 1=Negative, 2=Neutral, 3=Positive, 4=Very Positive
```

## Applications

- 📊 **Stock Market Analysis**: Analyze sentiment of NEPSE-related news
- 📱 **Social Media Monitoring**: Track public sentiment about stocks
- 🤖 **Trading Bots**: Incorporate sentiment signals
- 📰 **News Analysis**: Classify financial news sentiment
- 📈 **Investment Research**: Gauge market sentiment

## Limitations

- Trained specifically on financial/stock market terminology
- Performance may vary on general Nepali text
- Requires preprocessing for optimal results
- Best suited for short to medium-length texts

## Training Process

1. **Data Collection**: 500+ samples per sentiment class
2. **Augmentation**: Expanded to 2,986 total samples
3. **Architecture**: BiLSTM for sequence + CNN for n-grams
4. **Optimization**: Adam optimizer with early stopping
5. **Validation**: Stratified k-fold cross-validation

## Citation

```bibtex
@misc{nepse-sentiment-2025,
  title={NEPSE Sentiment Analyzer: BiLSTM-CNN for Nepali Financial Text},
  author={Your Name},
  year={2025},
  publisher={Hugging Face},
  url={https://huggingface.co/your-username/nepse-sentiment-analyzer}
}
```

## License

MIT License - Feel free to use for research and commercial applications.

## Contact

For questions or collaboration opportunities, please reach out via GitHub or Hugging Face discussions.

---

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