hindi-sentiment-analysis

Hindi Sentiment Analysis Model

This repository contains a Hindi sentiment analysis model that can classify text into three categories: negative (neg), neutral (neu), and positive (pos). The model has been trained and evaluated using various BERT-based architectures, with XLM-RoBERTa showing the best performance.

Model Performance

Test Accuracy Comparison

Our extensive evaluation shows:

• XLM-RoBERTa: 81.3%
• mBERT: 76.5%
• Custom-BERT-Attention: 74.9%
• IndicBERT: 69.9%

Detailed Results

Confusion Matrices

The confusion matrices show the prediction performance for each model:

• XLM-RoBERTa shows the strongest performance with 82.1% accuracy on positive class
• mBERT demonstrates balanced performance across classes
• Custom-BERT-Attention maintains consistent performance
• IndicBERT shows room for improvement in negative class detection

Per-class Metrics

The detailed per-class metrics show:

1. Precision:

   • Positive class: Best performance across all models (~0.80-0.85)
   • Neutral class: Consistent performance (~0.75-0.80)
   • Negative class: More varied performance (~0.40-0.70)

2. Recall:

   • Positive class: High recall across models (~0.85-0.90)
   • Neutral class: Moderate recall (~0.65-0.85)
   • Negative class: Lower but improving recall (~0.25-0.60)

3. F1-Score:

   • Positive class: Best overall performance (~0.80-0.85)
   • Neutral class: Good balance (~0.70-0.80)
   • Negative class: Area for potential improvement (~0.30-0.65)

Training Progress

The training graphs show:

• Consistent loss reduction across epochs
• Stable validation accuracy improvement
• No significant overfitting
• XLM-RoBERTa achieving the best validation accuracy
• Custom-BERT-Attention showing rapid initial learning

Model Usage

from transformers import AutoModelForSequenceClassification, AutoTokenizer

# Load the model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("madhav112/hindi-sentiment-analysis")
model = AutoModelForSequenceClassification.from_pretrained("madhav112/hindi-sentiment-analysis")

# Example usage
text = "यह फिल्म बहुत अच्छी है"
inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True)
outputs = model(**inputs)
predictions = outputs.logits.argmax(-1)

Model Architecture

The repository contains experiments with multiple BERT-based architectures:

1. XLM-RoBERTa (Best performing)

   • Highest overall accuracy
   • Best performance on positive sentiment
   • Strong cross-lingual capabilities

2. mBERT

   • Good balanced performance
   • Strong on neutral class detection
   • Consistent across all metrics

3. Custom-BERT-Attention

   • Competitive performance
   • Quick convergence during training
   • Good precision on positive class

4. IndicBERT

   • Baseline performance
   • Room for improvement
   • Better suited for specific Indian language tasks

Dataset

The model was trained on a Hindi sentiment analysis dataset with three classes:

• Positive (pos)
• Neutral (neu)
• Negative (neg)

The confusion matrices show balanced class distribution and strong performance across categories.

Training Details

The model was trained for 7 epochs with the following characteristics:

• Learning rate: Optimized for each architecture
• Batch size: Adjusted for optimal performance
• Validation split: Regular evaluation during training
• Early stopping: Monitored for best model selection
• Loss function: Cross-entropy loss

Limitations

• Lower performance on negative sentiment detection compared to positive
• Neutral class classification shows moderate confusion with both positive and negative
• Performance may vary on domain-specific text
• Best suited for standard Hindi text; may have reduced performance on heavily colloquial or dialectal variations

Citation

If you use this model in your research, please cite:

@misc{madhav2024hindisentiment,
  author = {Madhav},
  title = {Hindi Sentiment Analysis Model},
  year = {2024},
  publisher = {HuggingFace},
  howpublished = {\url{https://huggingface.co/madhav112/hindi-sentiment-analysis}}
}

Author

Madhav

• HuggingFace: madhav

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

Special thanks to the HuggingFace team and the open-source community for providing the tools and frameworks that made this model possible.

language: hi tags:

• hindi
• sentiment-analysis
• text-classification
• bert datasets:
• hindi-sentiment metrics:
• accuracy
• f1
• precision
• recall model-index:
• name: hindi-sentiment-analysis results:
  • task: type: text-classification name: Text Classification dataset: name: Hindi Sentiment type: hindi-sentiment metrics:
    • type: accuracy value: 81.3 name: Test Accuracy
    • type: f1 value: 0.82 name: F1 Score