game-reviews-sentiment

Wm-Grsa-Bilingual-Xgboost - Game Review Sentiment Analysis

Model Description

This model performs sentiment analysis on game reviews, classifying them into three categories:

• Positive: Favorable reviews
• Mixed: Neutral or mixed sentiment reviews
• Negative: Unfavorable reviews

Model Type: Wm-Grsa-Bilingual-Xgboost

Training Date: 2025-12-23

Performance

Test Set Metrics

Training Information

• Training Time: 274.84 seconds
• Training Samples: 629,884
• Validation Samples: 78,735
• Test Samples: 78,737

Model Configuration

{
  "model_name": "XGBoost",
  "embedding_model": "Lajavaness/bilingual-embedding-small",
  "n_estimators": 5000,
  "max_depth": 4,
  "learning_rate": 0.1,
  "subsample": 0.8,
  "colsample_bytree": 0.8,
  "subset": 1.0
}

Usage

Loading the Model

from pathlib import Path
import pickle

# Load the model components
model_dir = Path("path/to/model")

with open(model_dir / 'vectorizer.pkl', 'rb') as f:
    vectorizer = pickle.load(f)

with open(model_dir / 'classifier.pkl', 'rb') as f:
    classifier = pickle.load(f)

with open(model_dir / 'label_encoder.pkl', 'rb') as f:
    label_encoder = pickle.load(f)

Making Predictions

# Example reviews
reviews = [
    "This game is absolutely amazing! Best game I've played this year.",
    "It's okay, nothing special but not terrible either.",
    "Terrible game, waste of money and time."
]

# Transform and predict
X = vectorizer.transform(reviews)
predictions_encoded = classifier.predict(X)
predictions = label_encoder.inverse_transform(predictions_encoded)

print(predictions)
# Output: ['positive', 'mixed', 'negative']

# Get probabilities
probabilities = classifier.predict_proba(X)
print(probabilities)

Per-Class Performance

Feature Importance

The model identifies important words/phrases for each sentiment class. See results.json for the complete feature importance analysis.

Limitations

• The model is trained specifically on game reviews and may not generalize well to other domains
• Performance may vary on reviews with sarcasm or nuanced sentiments
• The model treats text as bag-of-words and doesn't capture word order

Training Details

This model was trained as part of a game review sentiment analysis project. For more information, see the project repository.

Files

• vectorizer.pkl: TF-IDF vectorizer
• classifier.pkl: Trained classifier
• label_encoder.pkl: Label encoder for sentiment classes
• config.json: Model configuration
• results.json: Complete training results and metrics

Citation

If you use this model, please cite:

@misc{game_review_sentiment,
  author = {Game Review Sentiment Analysis Project},
  title = {Sentiment Analysis Model for Game Reviews},
  year = {2025},
  url = {https://huggingface.co/wm-grsa-bilingual-xgboost}
}