{
  "embed_dim": 128,
  "hidden_dim": 128,
  "learning_rate": 0.0005,
  "batch_size": 64,
  "epochs": 20,
  "max_len": 200,
  "vocab_size": 73738,
  "dropout_rate": 0.5,
  "fc_sizes": [
    64,
    64,
    16
  ],
  "subset": 1.0
}

Usage

Loading the Model

python

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

python

# 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

Class	Precision	Recall	F1-Score	Support
Positive	0.9148	0.8990	0.9068	45859
Mixed	0.5301	0.5493	0.5395	12697
Negative	0.8232	0.8366	0.8298	20181

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-lstm-baseline}
}

auphong2707/wm-grsa-lstm-baseline-results

Wm-Grsa-Lstm-Baseline - Game Review Sentiment Analysis

Model Description

Performance

Test Set Metrics

Training Information

Model Configuration