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# 💬 Sentiment-Analysis-for-Product-Release-Sentiment
A **BERT-based sentiment analysis model** fine-tuned on a product review dataset. It predicts the sentiment of a text as **Positive**, **Neutral**, or **Negative** with a confidence score. This model is ideal for analyzing customer feedback, reviews, or user comments.
---
## ✨ Model Highlights
- 📌 **Architecture**: Based on [`bert-base-uncased`](https://huggingface.co/bert-base-uncased) by Google
- 🔧 **Fine-tuned** on labeled product review data
- 🔍 **3-way sentiment classification**: `Negative (0)`, `Neutral (1)`, `Positive (2)`
- 💾 **Quantized version available** for faster inference
---
## 🧠 Intended Uses
- ✅ Classifying product feedback and user reviews
- ✅ Sentiment analysis for e-commerce platforms
- ✅ Social media monitoring and customer opinion mining
---
## 🚫 Limitations
- ❌ Designed for English texts only
- ❌ May not perform well on sarcastic or ironic inputs
- ❌ May struggle with domains very different from product reviews
- ❌ Input texts longer than 128 tokens are truncated
---
## 🏋️♂️ Training Details
- **Base Model**: `bert-base-uncased`
- **Dataset**: Custom-labeled product review dataset
- **Epochs**: 5
- **Batch Size**: 8
- **Max Length**: 128 tokens
- **Optimizer**: AdamW
- **Loss Function**: CrossEntropyLoss (with class balancing)
- **Hardware**: Trained on NVIDIA GPU (CUDA-enabled)
---
## 📊 Evaluation Metrics
| Metric | Score |
|------------|-------|
| Accuracy | 0.90 |
| F1 | 0.90 |
| Precision | 0.90 |
| Recall | 0.90 |
---
## 🔎 Label Mapping
| Label ID | Sentiment |
|----------|-----------|
| 0 | Negative |
| 1 | Neutral |
| 2 | Positive |
---
## 🚀 Usage Example
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification, Trainer, TrainingArguments
from transformers import DataCollatorWithPadding
import torch
import torch.nn.functional as F
# Load model and tokenizer
model_name = "AventIQ-AI/Sentiment-Analysis-for-Product-Release-Sentiment"
tokenizer = BertTokenizer.from_pretrained(model_name)
model = BertForSequenceClassification.from_pretrained(model_name)
model.eval()
# Inference
def predict_sentiment(text):
inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
inputs = {k: v.to(quantized_model.device) for k, v in inputs.items()}
with torch.no_grad():
logits = quantized_model(**inputs).logits
probs = F.softmax(logits, dim=1)
predicted_class_id = torch.argmax(probs, dim=1).item()
confidence = probs[0][predicted_class_id].item()
label_map = {0: "Negative", 1: "Positive"}
label = label_map[predicted_class_id]
confidence_str = f"confidence : {confidence * 100:.1f}%"
return label, confidence_str
# Example
print(predict_sentiment(
"The service was excellent and the staff was friendly.")
)
```
---
## 🧪 Quantization
- Applied **post-training dynamic quantization** using PyTorch to reduce model size and speed up inference.
- Quantized model supports CPU-based deployments.
---
## 📁 Repository Structure
```
.
├── model/ # Quantized model files
├── tokenizer/ # Tokenizer config and vocabulary
├── model.safetensors/ # Fine-tuned full-precision model
├── README.md # Model documentation
```
---
## 📌 Limitations
- May not generalize to completely different domains (e.g., medical, legal)
- Quantized version may show slight drop in accuracy compared to full-precision model
---
## 🤝 Contributing
We welcome contributions! Please feel free to raise an issue or submit a pull request if you find a bug or have a suggestion. |