This project focuses on sentiment analysis of video game reviews, leveraging advanced natural language processing techniques to classify reviews as positive or negative. Despite the dataset's high imbalance (80% positive reviews), the project achieved significant accuracy improvements through data augmentation and model optimization.
- Source: Steam Games Reviews
- Nature: Highly imbalanced dataset (80% positive reviews)
- Utilized NLPaug for contextual data augmentation.
- Augmented minority class samples using RoBERTa to enhance class balance.
- LSTM (4 layers)
- Accuracy: 86%
- Bi-LSTM (3 layers)
- Accuracy: 85%
- BERT Sequence Classifier
- Trained classifier layers only: 82% accuracy
- BERT Sequence Classifier with LoRA
- Trained classifier layers with additional layers using LoRA (Low-Rank Adaptation) and 4-bit quantization: 92% accuracy
- Data Augmentation: Improved class balance with contextual augmentation using RoBERTa.
- Progressive Model Development:
- Transitioned from basic LSTM models to transformer-based architectures.
- Implemented LoRA for parameter-efficient fine-tuning.
- Optimized performance and resource utilization using 4-bit quantization.
- Achieved a significant accuracy boost (92%) with the advanced BERT-based approach.
- Python 3.8+
- PyTorch 1.12+
- Hugging Face Transformers
- NLPaug
- RoBERTa Pretrained Model
Install dependencies using:
pip install torch transformers nlpaug| Model | Accuracy |
|---|---|
| LSTM (4 layers) | 86% |
| Bi-LSTM (3 layers) | 85% |
| BERT Sequence Classifier | 82% |
| BERT + LoRA + 4-bit Quantization | 92% |
- Explore other transformer architectures like DeBERTa or DistilBERT.
- Fine-tune models on a broader set of game reviews from other platforms.
- Implement more robust augmentation strategies.
- Hugging Face for the Transformers library.
- Steam community for the dataset.
- NLPaug library for augmentation techniques.