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fitsblb/
YelpReviewsAnalyzer

Name: YelpReviewsAnalyzer
Availability: InStock

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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)