MemChainAI/adaptive-sentiment-classifier

Adaptive Sentiment Classifier

An improved sentiment analysis model using the adaptive-classifier library, designed for accurate classification of positive, negative, and neutral sentiments with special focus on technical and informational content.

Model Description

This model is based on the adaptive-classifier library and uses DistilBERT as the underlying transformer. It has been specifically trained to properly classify:

• Positive sentiment: Expressions of satisfaction, enthusiasm, approval
• Negative sentiment: Expressions of dissatisfaction, frustration, criticism
• Neutral sentiment: Factual information, questions, technical descriptions

Key Improvements

• ✅ Technical Content: Properly classifies technical descriptions as neutral
• ✅ Questions: Correctly identifies questions as neutral rather than negative
• ✅ Educational Content: Handles informational text appropriately
• ✅ Balanced Training: Uses detailed class descriptions for better embeddings

Training Data

• Primary Dataset: SetFit/tweet_sentiment_extraction (114 examples)
• Training Method: Adaptive classifier with continual learning
• Class Distribution: Balanced training with quality filtering
• Additional Features: Detailed class descriptions for stronger initial embeddings

Performance

• Test Accuracy: 80.0%
• Problematic Cases Resolved: 8/10 challenging examples correctly classified
• Improvement: 100% increase from baseline accuracy

Benchmark Examples

Text	Expected	Predicted	✓
"Granite Guardian 4 is a type of AI model..."	neutral	neutral	✅
"Do you know what Granite Guardian 4 is?"	neutral	neutral	✅
"Learning is a process of gaining knowledge..."	neutral	neutral	✅
"I love this new technology!"	positive	positive	✅
"This is terrible and I hate it."	negative	negative	✅

Usage

Installation

pip install adaptive-classifier

Basic Usage

from adaptive_classifier import AdaptiveClassifier

# Load the model
classifier = AdaptiveClassifier.from_pretrained("MemChainAI/adaptive-sentiment-classifier")

# Make predictions
text = "This is a great product!"
predictions = classifier.predict(text)

# Get top prediction
label, confidence = predictions[0]
print(f"Sentiment: {label} ({confidence:.3f})")

API Integration

This model is designed to work with the MemChain Models API:

import requests

response = requests.post(
    "http://localhost:8033/model/sentiment/predict",
    json={"text": "Your text here", "k": 3}
)
result = response.json()

Batch Processing

texts = [
    "I love this!",
    "This is terrible.",
    "The system processes data automatically."
]

# Batch prediction
batch_results = classifier.predict_batch(texts)
for i, predictions in enumerate(batch_results):
    label, confidence = predictions[0]
    print(f"Text {i+1}: {label} ({confidence:.3f})")

Training Methodology

1. Class Descriptions: Started with detailed descriptions of each sentiment class
2. Quality Examples: Used filtered, high-quality examples from the dataset
3. Iterative Training: Added examples gradually with evaluation at each step
4. Continual Learning: Leveraged adaptive classifier's continual learning capabilities

Intended Use

• Content Moderation: Analyze user-generated content sentiment
• Customer Feedback: Classify customer reviews and feedback
• Social Media: Monitor social media sentiment
• Technical Documentation: Properly classify technical content as neutral
• Educational Content: Handle informational and educational text appropriately

Limitations

• Optimized for English text
• Best performance on text similar to training data (tweets, reviews, questions)
• May require additional examples for domain-specific terminology
• Performance may vary on very long texts (>200 characters)

Ethical Considerations

• The model should not be used as the sole basis for important decisions
• Bias may exist reflecting the training data
• Regular evaluation and retraining recommended for production use
• Consider cultural and contextual factors when interpreting results

Citation

@misc{adaptive-sentiment-classifier-2025,
  title={Adaptive Sentiment Classifier},
  author={MemChain AI},
  year={2025},
  publisher={Hugging Face},
  url={https://huggingface.co/MemChainAI/adaptive-sentiment-classifier}
}

License

MIT License - see LICENSE file for details.

Contact

For questions, issues, or contributions, please visit the MemChain AI.