metadata
language: en
license: apache-2.0
library_name: transformers
tags:
- sentiment-analysis
- roberta
- amazon-reviews
- e-commerce
datasets:
- amazon_fine_food_reviews
metrics:
- accuracy
- f1
pipeline_tag: text-classification
Model Card: Amazon Sentiment RoBERTa Base
Model Description
This model is a fine-tuned version of RoBERTa-base specifically optimized for sentiment analysis of customer reviews. It was trained on a balanced subset of the Amazon Fine Food Reviews dataset to classify text into three distinct categories: Negative, Neutral, and Positive.
- Model Type: Transformer-based Text Classification
- Language: English
- Base Model:
roberta-base
Intended Use
- Primary Use Case: Real-time sentiment tracking for e-commerce platforms.
- Scope: Analyzing short to medium-length customer feedback and product reviews.
- Out-of-Scope: Not recommended for legal documents, medical advice, or languages other than English.
Training Data & Methodology
Dataset
- Source: Amazon Fine Food Reviews (Kaggle).
- Preprocessing: - Removal of duplicates and HTML tags.
- POS-tag-based Lemmatization for linguistic normalization.
- Undersampling to 15,000 samples (5,000 per class) to handle class imbalance.
- Labels: -
0: Negative (1-2 stars)1: Neutral (3 stars)2: Positive (4-5 stars)
Hyperparameters
- Learning Rate: 2e-5
- Batch Size: 16
- Epochs: 2
- Weight Decay: 0.01
- Max Sequence Length: 128 tokens
Performance Metrics
The model was evaluated on a held-out test set (20% of the balanced data):
| Metric | Value |
|---|---|
| Accuracy | 78.0% |
| Weighted F1-Score | 0.78 |
| Precision (Positive) | 0.83 |
| Recall (Positive) | 0.89 |
Key Strengths
- Contextual Understanding: Successfully handles complex structures, such as negation and sarcasm (e.g., "Don't listen to the haters, this is great!").
- Robustness: Significantly outperforms traditional TF-IDF and DistilBERT baselines in identifying ambiguous "Neutral" reviews.
Limitations & Bias
- Neutral Class: Still remains the most frequent source of misclassification due to the inherent subjectivity of 3-star ratings.
- Domain Specificity: Performance may vary when applied to domains outside of food and beverages (e.g., electronics or fashion).
- Sarcasm: While improved, extremely subtle sarcasm may still lead to errors.
How to Use
from transformers import pipeline
# Load the model directly from the Hub
model_path = "mlklt3/amazon-sentiment-roberta-base"
sentiment_pipeline = pipeline("sentiment-analysis", model=model_path)
# Example usage
text = "The product was okay, but I expected much better flavor for this price."
result = sentiment_pipeline(text)
print(result)
Citation
If you use this model in your research or project, please credit the Amazon Fine Food Reviews dataset and the Hugging Face Transformers library.