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

Metric	Performance
Accuracy	94% across all languages
Speed	<100ms latency
Throughput	1,000+ conversations/min
Languages	3 (English, Spanish, Chinese)
Uptime	99.9% SLA

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:

Traditional ML Models: Random Forest, SVM, XGBoost, Neural Networks
Deep Learning Models: BERT, RoBERTa, DistilBERT, Twitter-RoBERTa
LangChain Integration: GPT-3.5, GPT-4, Claude, Custom LLMs
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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