fabsa-roberta-sentiment

FABSA RoBERTa Sentiment Analysis Model

<div align="center"> <img src="https://img.shields.io/badge/Accuracy-93.97%25-brightgreen" alt="Accuracy"> <img src="https://img.shields.io/badge/Model-RoBERTa-blue" alt="Model"> <img src="https://img.shields.io/badge/Task-ABSA-orange" alt="Task"> </div>

📊 Model Overview

This is a fine-tuned RoBERTa model for Aspect-Based Sentiment Analysis (ABSA) on food delivery reviews, achieving 93.97% accuracy on the validation set. The model analyzes customer reviews across multiple specific aspects like food quality, delivery service, pricing, and more.

🎯 What is Aspect-Based Sentiment Analysis?

Unlike traditional sentiment analysis that gives one overall sentiment, ABSA identifies sentiment for specific aspects of a product or service. For example:

"The food was amazing but delivery took forever"

• Food aspect: ✅ Positive
• Delivery aspect: ❌ Negative

This granular analysis helps businesses identify exactly what customers love and what needs improvement.

🚀 Quick Start

Using the Model

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

# Load model and tokenizer
model_name = "Anudeep-Narala/fabsa-roberta-sentiment"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

# Example: Analyze a review
review = "The food was delicious but the delivery was slow"
aspect = "delivery"  # Can be: food, delivery, service, price, interface, overall

# Format input
input_text = f"Review: {review} | Aspect: {aspect}"
inputs = tokenizer(input_text, return_tensors="pt", truncation=True, max_length=256)

# Get prediction
with torch.no_grad():
    outputs = model(**inputs)
    predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
    predicted_class = torch.argmax(predictions, dim=-1).item()
    confidence = predictions[0][predicted_class].item()

# Map prediction to sentiment
sentiment_map = {0: "negative", 1: "neutral", 2: "positive"}
print(f"Aspect: {aspect}")
print(f"Sentiment: {sentiment_map[predicted_class]}")
print(f"Confidence: {confidence:.2%}")

Batch Processing Multiple Aspects

def analyze_review(review_text, aspects=["food", "delivery", "service", "price"]):
    """Analyze a review across multiple aspects"""
    results = {}

    for aspect in aspects:
        input_text = f"Review: {review_text} | Aspect: {aspect}"
        inputs = tokenizer(input_text, return_tensors="pt", truncation=True, max_length=256)

        with torch.no_grad():
            outputs = model(**inputs)
            predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
            predicted_class = torch.argmax(predictions, dim=-1).item()
            confidence = predictions[0][predicted_class].item()

        sentiment_map = {0: "negative", 1: "neutral", 2: "positive"}
        results[aspect] = {
            "sentiment": sentiment_map[predicted_class],
            "confidence": confidence
        }

    return results

# Example usage
review = "Great food and reasonable prices, but the app keeps crashing"
results = analyze_review(review)

for aspect, result in results.items():
    print(f"{aspect.capitalize()}: {result['sentiment']} (confidence: {result['confidence']:.2%})")

📈 Performance Metrics

🎯 Supported Aspects

The model is trained to analyze sentiment for these specific aspects:

1. food - Food quality, taste, freshness, presentation
2. delivery - Delivery speed, reliability, driver behavior, packaging
3. service - Customer support, staff attitude, responsiveness
4. price - Value for money, fees, discounts, pricing fairness
5. interface - App/website usability, navigation, features
6. overall - General satisfaction and overall experience

🏷️ Sentiment Classes

• Positive (2): Favorable opinion, satisfaction, praise
• Neutral (1): Mixed feelings, objective statements, neutral tone
• Negative (0): Complaints, dissatisfaction, criticism

🛠️ Technical Details

Model Architecture

• Base Model: cardiffnlp/twitter-roberta-base-sentiment-latest
• Architecture: RoBERTa (Robustly Optimized BERT Pretraining Approach)
• Model Type: Encoder-based transformer
• Number of Parameters: ~125M
• Fine-tuning Task: Sequence Classification (3 classes)

Training Configuration

• Epochs: 3
• Learning Rate: 8e-6 with cosine restarts
• Batch Size: 16
• Max Sequence Length: 128 tokens
• Optimizer: AdamW
• Framework: PyTorch + Hugging Face Transformers

Dataset

• Name: jordiclive/FABSA
• Domain: FABSA, An aspect-based sentiment analysis dataset in the Customer Feedback space (Trustpilot, Google Play and Apple Store reviews).
• Training Set: 13,998 labeled examples
• Validation Set: 1,858 examples
• Test Set: 1,587 examples (reserved)
• Languages: English
• Annotation: Aspect-level sentiment labels

💡 Use Cases

Business Intelligence

• Customer Feedback Analysis: Automatically categorize and analyze thousands of reviews
• Competitive Analysis: Compare sentiment across platforms and competitors
• Product Development: Identify which aspects need improvement
• Quality Monitoring: Track sentiment trends over time

Real-time Applications

• Dashboard Analytics: Build live sentiment monitoring dashboards
• Alert Systems: Trigger alerts when negative sentiment spikes
• Customer Support: Prioritize reviews that need immediate attention
• A/B Testing: Measure impact of changes on specific aspects

Research

• Sentiment Analysis Studies: Benchmark against other ABSA models
• Multi-aspect Learning: Study aspect-specific sentiment patterns
• Transfer Learning: Fine-tune for other domains (e-commerce, hospitality)

📊 Example Results

review = "Amazing pizza and great prices! The delivery was fast but the driver was rude."

Analysis Output:

• 🍕 Food: Positive (98.5% confidence)
• 💰 Price: Positive (94.2% confidence)
• 🚚 Delivery: Negative (87.6% confidence)
• 👤 Service: Negative (91.3% confidence)

This granular insight shows that while the product and pricing are excellent, there are service issues that need addressing.

🔧 Installation

pip install transformers torch

For production environments with GPU acceleration:

pip install transformers torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118

⚡ Performance Tips

1. Batch Processing: Process multiple reviews at once for better throughput
2. GPU Acceleration: Use CUDA for ~10x faster inference
3. Model Quantization: Use quantization for reduced memory footprint
4. ONNX Export: Convert to ONNX for optimized production deployment

# Enable GPU if available
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)

🔄 Model Evolution

This model represents the final iteration of extensive experimentation:

1. Red-Pajama-7B: 8% accuracy (decoder limitations for classification)
2. DialoGPT-small: 51.5% (baseline)
3. RoBERTa Basic: 86% (initial fine-tuning)
4. RoBERTa Enhanced: 90.7% (improved hyperparameters)
5. RoBERTa Neutral-focused: 91.7% (class imbalance handling)
6. RoBERTa Final: 93.97% ✅ (optimal configuration)

📚 Related Resources

• GitHub Repository: aspect-based-sentiment-analysis
• Interactive Demo: See the repository for visualization dashboard
• Dataset Schema: CSV format with aspect-level annotations
• Training Code: Available in the repository

📄 Citation

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

@misc{narala2025fabsa,
  author = {Anudeep Reddy Narala},
  title = {FABSA RoBERTa: Fine-tuned Model for Aspect-Based Sentiment Analysis on Food Delivery Reviews},
  year = {2025},
  publisher = {HuggingFace},
  howpublished = {\url{https://huggingface.co/Anudeep-Narala/fabsa-roberta-sentiment}},
}

📧 Contact

• Author: Anudeep Reddy Narala
• Email: [email protected]
• GitHub: @Anudeepreddynarala

📜 License

This model is released under the MIT License. Feel free to use it for commercial and non-commercial applications.

🙏 Acknowledgments

• Base model: cardiffnlp/twitter-roberta-base-sentiment-latest
• Framework: Hugging Face Transformers
• Compute: Training performed on GPU infrastructure

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Ready to analyze your customer feedback? Try the model now! 🚀