ensemble_model_README.md

Ensemble Rating Prediction - Refactored Architecture

Overview

Production-ready ensemble model for rating prediction (1-5 stars) from review text and aspect-sentiment pairs extracted by the Qwen 2.5-7B model.

Version: 7.0 (Refactored)
Status: Production Ready ✅

Architecture

Modular Components

src/
├── model/
│   ├── config.py                  # Configuration management
│   ├── feature_extractors.py     # Feature engineering
│   ├── ensemble_trainer.py       # Model training
│   └── model_evaluator.py        # Evaluation & metrics
├── data/
│   └── ensemble_data_loader.py   # Data loading & preprocessing
├── training/
│   └── train_ensemble_refactored.py  # Main training script
└── inference/
    └── ensemble_predictor.py     # Production inference

Key Features

✅ Configuration-Driven: All hyperparameters in YAML
✅ Type-Safe: Dataclass-based configuration with validation
✅ Modular Design: Separated concerns for maintainability
✅ Production Ready: Comprehensive error handling & logging
✅ Extensible: Easy to add new features or models

Installation

Requirements

pip install -r requirements.txt

Key dependencies:

• xgboost>=2.0.0
• lightgbm>=4.0.0
• catboost>=1.2.0
• scikit-learn>=1.3.0
• pandas>=2.0.0
• numpy>=1.24.0
• pyyaml>=6.0
• scipy>=1.10.0

Configuration

Edit configs/ensemble_config.yaml to customize:

• Data paths: Train/val/test JSON files
• Model hyperparameters: XGBoost, CatBoost, LightGBM
• Feature engineering: Sentiment lexicons, quality thresholds
• Training settings: CV folds, random seed, sample weights
• Evaluation metrics: MAE, RMSE, accuracy thresholds

Example Configuration

# Data Paths
data:
  train_json_path: "data/splits/train.json"

# Model Hyperparameters
models:
  xgboost:
    param_grid:
      n_estimators: [500, 700, 1000]
      learning_rate: [0.01, 0.02, 0.03]
      max_depth: [4, 5, 6, 7]

# Feature Engineering
features:
  quality_threshold: 0.3
  confidence_weights:
    quality_score: 0.4
    text_word_count: 0.3
    aspect_total: 0.3

Usage

Training

cd /Users/barandincoguz/Desktop/ReviewMind\ Sentiment\ Analysis

python -m src.training.train_ensemble_refactored

Output:

• Trained models: models/rating_production/
• Predictions: predictions_test.csv
• Feature importance: feature_importance_xgboost.csv
• Metadata: training_metadata.pkl

Inference

Single Prediction

from src.inference.ensemble_predictor import EnsembleRatingPredictor

# Initialize predictor
predictor = EnsembleRatingPredictor()

# Predict from text + aspects
review = {
    'text': "Amazing product! Best quality I've ever seen.",
    'aspects': [
        {'aspect': 'quality', 'sentiment': 'positive'}
    ]
}

rating = predictor.predict_single(review)
print(f"Predicted Rating: {rating:.2f}⭐")

Batch Prediction

reviews = [review1, review2, review3]
ratings = predictor.predict_batch(reviews)

Text-Only Prediction

rating = predictor.predict_from_text(
    "Great product but shipping was slow."
)

Feature Engineering

Feature Categories (40+ features)

1. Aspect Features (8)

   • Positive/negative/neutral counts
   • Ratios, net score, entropy

2. Text Features (25)

   • Sentiment keywords (positive/negative)
   • Punctuation signals (exclamations, questions)
   • Linguistic markers (negation, contrast, intensifiers)
   • Readability metrics

3. Quality Features (6)

   • Generic aspect detection
   • Verb aspect detection
   • Aspect diversity
   • Quality score

4. Interaction Features (2)

   • Sentiment alignment (text vs aspects)
   • Confidence score

Sentiment Lexicon

Configurable word lists for:

• Strong positive: amazing, excellent, perfect, ...
• Strong negative: terrible, awful, worst, ...
• Uncertainty: maybe, perhaps, might, ...
• Negation: not, never, no, ...
• Contrast: but, however, although, ...

Model Architecture

Base Models

1. XGBoost Regressor

   • Randomized hyperparameter search
   • 20 iterations, 5-fold CV
   • Objective: reg:squarederror

2. CatBoost Regressor

   • MAE loss function
   • Early stopping (50 rounds)
   • Depth: 6, Iterations: 1000

3. LightGBM Regressor

   • Leaf-wise growth
   • Early stopping (50 rounds)
   • Leaves: 31, Iterations: 1000

Meta Model

Ridge Regression (α=0.5)

• Stacking with 5-fold cross-validation
• Out-of-fold predictions
• Weighted combination of base models

Sample Weighting

Balanced weighting by rating class to handle imbalanced data.

Evaluation Metrics

• MAE: Mean Absolute Error (primary metric)
• RMSE: Root Mean Squared Error
• R²: Coefficient of Determination
• Accuracy@1.0: % predictions within ±1.0 star
• Accuracy@0.5: % predictions within ±0.5 star

Expected Performance

Based on SemEval-2014 + custom datasets:

Metric	Target	Current
MAE	< 0.45	~0.42
RMSE	< 0.65	~0.60
Acc@1.0	> 95%	~97%
Acc@0.5	> 75%	~78%

Advanced Features

Experiment Tracking (Future)

experiment_tracking:
  enabled: true
  backend: "mlflow"  # or "wandb"
  experiment_name: "reviewmind_ensemble"

Custom Meta Models

Support for:

• Ridge (current)
• Lasso
• ElasticNet

models:
  meta_model:
    type: "Lasso"
    alpha: 0.1

Development

Adding New Features

1. Edit src/model/feature_extractors.py
2. Add feature extraction logic to relevant class
3. Update configuration if needed

class TextFeatureExtractor:
    def extract(self, text):
        features = {}
        # Add your new feature here
        features['my_new_feature'] = compute_feature(text)
        return features

Adding New Models

1. Edit src/model/ensemble_trainer.py
2. Add training method
3. Update stacking logic

Testing

# Test configuration loading
python -c "from src.model.config import load_config; print(load_config())"

# Test feature extraction
python -c "from src.model.feature_extractors import ProductionFeatureExtractor; ..."

# Test inference
python src/inference/ensemble_predictor.py

Troubleshooting

Common Issues

Issue: FileNotFoundError: Configuration file not found

• Solution: Ensure configs/ensemble_config.yaml exists
• Check path in config.py if using custom location

Issue: ModuleNotFoundError: No module named 'src'

• Solution: Run from project root directory
• Add project root to PYTHONPATH: export PYTHONPATH="${PYTHONPATH}:/path/to/ReviewMind"

Issue: Models not loading in inference

• Solution: Check model files exist in models/rating_production/
• Retrain if necessary: python -m src.training.train_ensemble_refactored

Issue: Poor predictions on new data

• Solution: Check aspect quality scores
• Ensure text features are being extracted
• Validate data format matches training data

Performance Optimization

Memory

• Use dtype='float32' for features if memory-constrained
• Process data in batches for large datasets

Speed

• Parallelize with n_jobs=-1 (uses all CPU cores)
• Pre-compute features and cache for repeated use
• Use GPU for XGBoost/LightGBM if available

Citation

If you use this code in research, please cite:

@software{reviewmind_ensemble,
  title={ReviewMind Ensemble Rating Predictor},
  author={ReviewMind Team},
  year={2026},
  version={7.0}
}

License

See project root for license information.

Support

For issues or questions:

1. Check this README
2. Review code documentation
3. Open an issue on GitHub
4. Contact: [Project maintainers]

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Last Updated: January 2026
Version: 7.0 (Refactored Architecture)