💬 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.

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✨ Model Highlights

• 📌 Architecture: Based on 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

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🧠 Intended Uses

• ✅ Classifying product feedback and user reviews
• ✅ Sentiment analysis for e-commerce platforms
• ✅ Social media monitoring and customer opinion mining

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🚫 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

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🏋️‍♂️ 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)

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📊 Evaluation Metrics

Metric	Score
Accuracy	0.90
F1	0.90
Precision	0.90
Recall	0.90

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🔎 Label Mapping

Label ID	Sentiment
0	Negative
1	Neutral
2	Positive

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🚀 Usage Example

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.")
)

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🧪 Quantization

• Applied post-training dynamic quantization using PyTorch to reduce model size and speed up inference.
• Quantized model supports CPU-based deployments.

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📁 Repository Structure

.
├── model/               # Quantized model files
├── tokenizer/           # Tokenizer config and vocabulary
├── model.safetensors/   # Fine-tuned full-precision model
├── README.md            # Model documentation

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📌 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

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🤝 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.