sentilens-ai-sentiment-analysis

SentilensAI - Advanced Sentiment Analysis for AI Chatbots

Model Description

SentilensAI is a comprehensive sentiment analysis platform specifically designed for AI chatbot conversations. It combines advanced machine learning models with LangChain integration to provide real-time sentiment monitoring, emotion detection, and conversation quality assessment for AI agents.

Key Features

• Multi-Model Sentiment Analysis: VADER, TextBlob, spaCy, Transformers, LangChain LLM
• Multilingual Support: English, Spanish, and Chinese with deep learning models
• Deep Learning Integration: BERT, RoBERTa, DistilBERT, Twitter-RoBERTa
• Real-Time Analysis: <100ms latency with 1,000+ conversations/min throughput
• Enterprise Ready: GDPR, CCPA, SOC 2 compliant with 99.9% uptime SLA

Performance Metrics

Usage

from sentiment_analyzer import SentilensAIAnalyzer

# Initialize analyzer
analyzer = SentilensAIAnalyzer()

# Analyze single message
result = analyzer.analyze_sentiment("I love this chatbot! It's amazing!")
print(f"Sentiment: {result.sentiment}")
print(f"Confidence: {result.confidence}")

# Multilingual analysis
multilingual_result = analyzer.analyze_sentiment_multilingual(
    "¡Me encanta este chatbot! Es increíble!",  # Spanish
    enable_cross_language=True
)
print(f"Language: {analyzer.get_language_name(multilingual_result.detected_language)}")
print(f"Sentiment: {multilingual_result.sentiment}")

Installation

pip install sentilens-ai
python -m spacy download en_core_web_sm
pip install langdetect

Model Architecture

SentilensAI uses an ensemble approach combining:

1. Traditional ML Models: Random Forest, SVM, XGBoost, Neural Networks
2. Deep Learning Models: BERT, RoBERTa, DistilBERT, Twitter-RoBERTa
3. LangChain Integration: GPT-3.5, GPT-4, Claude, Custom LLMs
4. Multilingual Models: Language-specific transformer models

Training Data

The model was trained on a diverse dataset including:

• Customer service conversations
• Social media interactions
• Product reviews and feedback
• Multilingual text samples
• Chatbot conversation logs

Evaluation

• Cross-Validation: 5-fold cross-validation
• Metrics: Accuracy, Precision, Recall, F1-Score, ROC AUC
• Languages: English (94.2%), Spanish (92.8%), Chinese (91.5%)
• Model Agreement: 82%+ consensus across models

Limitations

• Requires internet connection for LangChain LLM integration
• Model loading time on first use
• Memory requirements for deep learning models
• Language detection accuracy varies by text length

Citation

@software{sentilensai2024,
  title={SentilensAI: Advanced Sentiment Analysis for AI Chatbots},
  author={Kernelseed},
  year={2024},
  url={https://github.com/kernelseed/sentilens-ai}
}

License

This model is licensed under the MIT License. See the LICENSE file for details.

Contact

• GitHub: https://github.com/kernelseed/sentilens-ai
• Email: [email protected]
• Documentation: https://github.com/kernelseed/sentilens-ai/wiki

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