license: cc-by-nc-4.0
tags:
- sentiment-analysis
- text-classification
- nlp
language:
- en
Amazon Reviews Sentiment Analysis Model
Model Description
This model is a sentiment analysis model fine-tuned using BertForSequenceClassification on the Amazon Reviews dataset. It classifies Amazon product reviews into sentiment categories: negative, neutral, or positive. Intended for research, educational, and non-commercial use only.
Base Model
- bert-base-uncased
- Architecture: Transformer (BERT)
- Head: Sequence Classification
Intended Use
✅ Allowed Uses
- Academic research
- Educational projects
- Personal learning
- Non-commercial applications
- Experiments and benchmarking
❌ Prohibited Uses
- Commercial use
- Selling or reselling the model
- Paid APIs or SaaS products
- Monetized applications or services
Commercial use is strictly prohibited under the CC BY-NC 4.0 license.
Training Data
Trained on the Amazon Reviews dataset:
- Language: English
- Domain: E-commerce product reviews
- Data type: Text reviews with sentiment labels
The original dataset creators retain all rights to the data. Users should consult the dataset’s original license for details.
Training Procedure
- Model: BertForSequenceClassification
- Framework: Hugging Face Transformers
- Number of labels: 3
- Loss Function: Cross-entropy loss
- Training performed on GPU if available, otherwise CPU
Label Mapping
| Label ID | Sentiment |
|---|---|
| 0 | Negative |
| 1 | Neutral |
| 2 | Positive |
Evaluation
Evaluated using a multi-class classification report with three categories:
- Negative
- Neutral
- Positive
Metrics include precision, recall, F1-score, and support (per class). Performance may vary depending on product category and review style.
Limitations and Bias
- Reflects biases in Amazon reviews
- May not perform well on non-product text
- Not suitable for non-English languages
- Predictions are subjective, not factual judgments
Ethical Considerations
Analyze subjective content only; not for high-stakes decisions.
How to Use
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
model_name = "mianzaka/sentiment-analysis-model"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
text = "The product quality is decent but delivery was slow."
inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
with torch.no_grad():
outputs = model(**inputs)
predicted_label = torch.argmax(outputs.logits, dim=1).item()
label_map = {0: "Negative", 1: "Neutral", 2: "Positive"}
print("Predicted sentiment:", label_map[predicted_label])
License
Released under CC BY-NC 4.0. Commercial use, resale, or monetization is prohibited. Full license: https://creativecommons.org/licenses/by-nc/4.0/
Citation
@misc{sentiment-analysis-model,
author = {Mian Zaka},
title = {Amazon Reviews Sentiment Analysis Model},
year = {2026},
publisher = {Hugging Face}
}
Contact
For questions or feedback, contact the model author via Hugging Face.