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