This project demonstrates the implementation of a sentiment analysis model to classify Amazon customer reviews as either positive or negative. It leverages cutting-edge NLP techniques using BERT (Bidirectional Encoder Representations from Transformers). The project introduces key concepts in natural language processing and deep learning, including:
- Text preprocessing and tokenization.
- Transformer-based architecture.
- Efficient handling of large datasets.
- Framework: PyTorch and Hugging Face Transformers.
- Dataset: Amazon Reviews (customer feedback on products)
- Training Data: 3.6 million reviews.
- Testing Data: 400,000 reviews.
- Each review contains a:
- Title: A brief summary of the review.
- Content: The full review text.
- Label: Sentiment (0 = Negative, 1 = Positive).
- Model Architecture:
- Preprocessing: BERT tokenizer for text encoding.
- Base Model: Pre-trained BERT (fine-tuned for sentiment classification).
- Output Layer: Fully connected layer for binary classification.
You can also open the notebook in Google Colab for interactive execution:
- Training Set: 96% accuracy.
- Testing Set: 94% accuracy.
- Learned the basics of neural networks and their components.
- Gained hands-on experience with transformer-based NLP models like BERT.
- Processed and analyzed millions of customer reviews.
- Learned the importance of data preprocessing for model performance.
- Visualized sentiment distributions and model predictions.
- Experiment with larger transformer models for better accuracy.
- Optimize preprocessing and inference for faster execution.
- Apply transfer learning to other domains, such as movie or hotel reviews.
Teja Gopal Reddy Vaka - Aspiring to become a top 1% AI professional.