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--- |
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license: mit |
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tags: |
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- sentiment-analysis |
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- aspect-based-sentiment-analysis |
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- absa |
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- product-reviews |
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- scikit-learn |
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language: |
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- en |
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metrics: |
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- accuracy |
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- f1 |
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library_name: sklearn |
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pipeline_tag: text-classification |
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--- |
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# AspectLens - Aspect-Based Sentiment Analysis |
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A production-ready **Aspect-Based Sentiment Analysis (ABSA)** model that extracts granular sentiment for specific product features from customer reviews. |
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## Model Description |
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This model identifies sentiment for 6 product aspects: |
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- **Battery**, **Camera**, **Display**, **Performance**, **Build**, **Price** |
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## Model Details |
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- **Architecture**: TF-IDF Vectorization + Logistic Regression |
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- **Training Data**: 200K Amazon mobile phone reviews |
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- **Accuracy**: 90% on test set |
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- **Inference Time**: <100ms per review |
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- **Language**: English |
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## Performance |
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| Metric | Score | |
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|--------|-------| |
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| Overall Accuracy | 90.0% | |
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| Positive Class | 100% | |
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| Negative Class | 80% | |
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| Neutral Class | 90% | |
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| Weighted F1 | 0.90 | |
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## Usage |
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```python |
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from inference import predict |
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# Analyze a review |
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review = "Great camera but terrible battery life. The display is amazing though!" |
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result = predict(review) |
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print(result) |
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# Output: |
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# { |
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# 'aspects': [ |
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# {'aspect': 'camera', 'sentiment': 'positive', 'confidence': 0.982}, |
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# {'aspect': 'battery', 'sentiment': 'negative', 'confidence': 0.947}, |
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# {'aspect': 'display', 'sentiment': 'positive', 'confidence': 0.991} |
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# ], |
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# 'overall_sentiment': 'positive' |
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# } |
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``` |
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## API Endpoint |
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Once deployed on Hugging Face Inference API: |
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```python |
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import requests |
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API_URL = "https://api-inference.huggingface.co/models/YOUR_USERNAME/AspectLens-Aspectwise-sentiment-breakdown" |
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headers = {"Authorization": "Bearer YOUR_HF_TOKEN"} |
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def query(payload): |
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response = requests.post(API_URL, headers=headers, json=payload) |
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return response.json() |
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output = query({ |
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"inputs": "Great phone but battery dies quickly" |
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}) |
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``` |
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## Training Details |
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- **Dataset**: Amazon Product Reviews (Mobile Phones) |
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- **Preprocessing**: Text cleaning, sentence segmentation, aspect detection |
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- **Features**: TF-IDF with max 5000 features |
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- **Classifier**: Logistic Regression with balanced class weights |
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- **Validation**: 80/20 train-test split |
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## Limitations |
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- English language only |
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- Focused on mobile phone reviews (generalizes to other electronics) |
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- Rule-based aspect detection (keyword matching) |
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- May struggle with sarcasm or complex negations |
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## Intended Use |
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- Product development teams identifying pain points |
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- Customer support prioritizing issues |
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- E-commerce platforms analyzing feedback |
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- Marketing teams understanding preferences |
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## Citation |
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```bibtex |
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@misc{aspectlens2026, |
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author = {Aryan Patel}, |
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title = {AspectLens: Aspect-Based Sentiment Analysis for Product Reviews}, |
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year = {2026}, |
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publisher = {Hugging Face}, |
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howpublished = {\url{https://huggingface.co/YOUR_USERNAME/AspectLens}} |
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} |
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``` |
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## License |
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MIT License - Free for commercial and research use |
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## Contact |
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- GitHub: [@Aryan1patel](https://github.com/Aryan1patel) |
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- Repository: [AspectLens](https://github.com/Aryan1patel/Aspectwise-sentiment-breakdown-and-Real-data-Insights) |
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--- |
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Built with ❤️ using scikit-learn |
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