amazon-review-sentiment-analysis
| Property | Value |
|---|---|
| License | Apache 2.0 |
| Training Data | 17,280 Amazon reviews |
| Validation Accuracy | 80% |
| Languages Supported | English, Dutch, German, French, Spanish, Italian |
What is amazon-review-sentiment-analysis?
This is a sophisticated sentiment analysis model built on DistilBERT architecture, specifically fine-tuned for analyzing product reviews from Amazon. The model specializes in predicting review ratings on a 1-5 star scale, leveraging a multilingual approach to handle reviews in six different languages. With over 5,000 downloads and trained on a substantial dataset of Amazon customer reviews, it represents a practical solution for e-commerce sentiment analysis.
Implementation Details
The model is implemented using PyTorch and the Transformers library, utilizing a DistilBERT base that's been fine-tuned with carefully selected hyperparameters. Training was conducted over 2 epochs with a learning rate of 2e-05 and a batch size of 16, employing the Adam optimizer with linear learning rate scheduling.
- Built on DistilBERT base architecture
- Trained on 17,280 reviews with 4,320 validation samples
- Achieves 80% accuracy on validation set
- Implements linear learning rate scheduling
Core Capabilities
- Multi-language sentiment analysis
- 1-5 star rating prediction
- Product review understanding
- Batch processing support
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its multilingual capabilities and specific optimization for e-commerce reviews, particularly from Amazon. Its ability to handle six different languages while maintaining 80% accuracy makes it particularly valuable for international e-commerce applications.
Q: What are the recommended use cases?
The model is ideal for: 1) Automated review rating prediction, 2) Customer sentiment analysis in e-commerce, 3) Multi-language review processing, and 4) Product feedback analysis. However, it's important to note that performance may be limited when applied outside the e-commerce domain.
