sentiment-distilbert-7class

DistilBERT 7-Class Sentiment Analysis Model

A fine-tuned DistilBERT model for nuanced sentiment analysis with 7 sentiment classes on a scale from -3 (Very Negative) to +3 (Very Positive).

Model Description

This model performs fine-grained sentiment classification, providing more nuanced predictions than traditional binary positive/negative models. It's particularly useful for business applications where understanding the intensity of sentiment matters (e.g., identifying "at-risk" customers vs. extremely dissatisfied ones).

Architecture: DistilBERT (distilbert-base-uncased)
Parameters: 66 million
Training Data: 6,000 IMDB movie reviews
Accuracy: 73.7%

Sentiment Classes

Usage

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

# Load model and tokenizer
model_id = "Thi144/sentiment-distilbert-7class"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForSequenceClassification.from_pretrained(model_id)

# Class mapping
CLASS_LABELS = {
    0: {"scale": -3, "label": "negative", "name": "Very Negative"},
    1: {"scale": -2, "label": "negative", "name": "Negative"},
    2: {"scale": -1, "label": "negative", "name": "Slightly Negative"},
    3: {"scale": 0, "label": "neutral", "name": "Neutral"},
    4: {"scale": 1, "label": "positive", "name": "Slightly Positive"},
    5: {"scale": 2, "label": "positive", "name": "Positive"},
    6: {"scale": 3, "label": "positive", "name": "Very Positive"}
}

# Predict sentiment
def predict_sentiment(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=512)
    
    with torch.no_grad():
        outputs = model(**inputs)
        predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
        class_id = predictions.argmax().item()
        confidence = predictions[0][class_id].item()
    
    result = CLASS_LABELS[class_id]
    return {
        "class": class_id,
        "scale": result["scale"],
        "label": result["label"],
        "name": result["name"],
        "confidence": confidence
    }

# Example
result = predict_sentiment("This movie was absolutely amazing!")
print(f"Sentiment: {result['name']} (Scale: {result['scale']}, Confidence: {result['confidence']:.2%})")

Performance Metrics

Overall Accuracy: 73.7%

Class-Specific Performance:

• Very Negative (-3): 81% precision, 88% recall
• Negative (-2): 83% precision, 77% recall
• Slightly Negative (-1): 54% precision, 58% recall
• Neutral (0): 86% precision, 64% recall
• Slightly Positive (+1): 58% precision, 54% recall
• Positive (+2): 79% precision, 83% recall
• Very Positive (+3): 88% precision, 81% recall

The model performs best at identifying strong sentiments (Very Negative/Positive) and struggles most with subtle distinctions (Slightly Negative/Positive).

Training Details

• Base Model: distilbert-base-uncased
• Dataset: 6,000 IMDB reviews (4,800 train, 1,200 test)
• Label Conversion: Binary labels converted to 7-class using text intensity analysis
• Epochs: 4
• Batch Size: 16
• Optimizer: AdamW (lr=2e-5)
• Training Time: ~15-20 minutes on CPU

Limitations

• Trained on movie reviews, may not generalize perfectly to other domains
• Slightly Negative/Positive classes have lower accuracy (~54-58%)
• Performance depends on text clarity and length
• May struggle with sarcasm or complex sentiment

Intended Use

Primary Use Cases:

• Customer feedback analysis with nuanced sentiment scoring
• Product review sentiment classification
• Social media monitoring with intensity detection
• Business intelligence dashboards requiring granular sentiment

Not Recommended For:

• Safety-critical applications
• Legal decision-making
• Medical diagnosis

License

Apache 2.0

Citation

If you use this model, please cite:

@model{thi144-sentiment-distilbert-7class,
  author = {Thi144},
  title = {DistilBERT 7-Class Sentiment Analysis},
  year = {2025},
  publisher = {HuggingFace},
  url = {https://huggingface.co/Thi144/sentiment-distilbert-7class}
}