| --- |
| language: en |
| license: mit |
| tags: |
| - sentiment-analysis |
| - text-classification |
| - yelp |
| - transformers |
| - distilbert |
| --- |
| |
| # Yelp Reviews Sentiment Analyzer |
|
|
| ## Model Overview |
| This is a DistilBERT-based sentiment analysis model fine-tuned on a subset of the Yelp Open Dataset. It classifies restaurant reviews into three categories: Negative, Neutral, and Positive. |
|
|
| ## Intended Use |
| - Sentiment classification of restaurant reviews for business insights, customer feedback analysis, or academic research. |
| - Can be integrated into applications to provide real-time sentiment detection. |
|
|
| ## Training Data |
| - Yelp Open Dataset (restaurant reviews subset). |
| - Labels derived from star ratings converted into sentiment classes. |
|
|
| ## Model Architecture |
| - Based on `distilbert-base-uncased`. |
| - Fine-tuned using Hugging Face's `AutoModelForSequenceClassification`. |
|
|
| ## Performance |
| - Accuracy: ~78.5% |
| - F1 Score: ~78.4% |
| - Precision: ~78.3% |
| - Recall: ~78.5% |
|
|
| ## Limitations |
| - Performance may vary on reviews from domains outside Yelp restaurants. |
| - Model is trained only on English-language reviews. |
| - Neutral class can be subjective, and borderline cases may be misclassified. |
|
|
| ## How to Use |
| Use Hugging Face Transformers pipeline: |
|
|
| ```python |
| from transformers import pipeline |
| |
| sentiment_analyzer = pipeline("sentiment-analysis", model="fitsblb/YelpReviewsAnalyzer") |
| |
| result = sentiment_analyzer("The food was amazing but the service was slow.") |
| print(result) |
| |
