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--- |
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license: mit |
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tags: |
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- sentiment-analysis |
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- nlp |
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- multilingual |
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- chatbot |
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- langchain |
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- deep-learning |
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- transformers |
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- bert |
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- roberta |
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- distilbert |
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pipeline_tag: text-classification |
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--- |
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# SentilensAI - Advanced Sentiment Analysis for AI Chatbots |
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## Model Description |
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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. |
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## Key Features |
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- **Multi-Model Sentiment Analysis**: VADER, TextBlob, spaCy, Transformers, LangChain LLM |
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- **Multilingual Support**: English, Spanish, and Chinese with deep learning models |
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- **Deep Learning Integration**: BERT, RoBERTa, DistilBERT, Twitter-RoBERTa |
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- **Real-Time Analysis**: <100ms latency with 1,000+ conversations/min throughput |
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- **Enterprise Ready**: GDPR, CCPA, SOC 2 compliant with 99.9% uptime SLA |
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## Performance Metrics |
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| Metric | Performance | |
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|--------|-------------| |
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| Accuracy | 94% across all languages | |
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| Speed | <100ms latency | |
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| Throughput | 1,000+ conversations/min | |
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| Languages | 3 (English, Spanish, Chinese) | |
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| Uptime | 99.9% SLA | |
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## Usage |
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```python |
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from sentiment_analyzer import SentilensAIAnalyzer |
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# Initialize analyzer |
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analyzer = SentilensAIAnalyzer() |
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# Analyze single message |
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result = analyzer.analyze_sentiment("I love this chatbot! It's amazing!") |
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print(f"Sentiment: {result.sentiment}") |
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print(f"Confidence: {result.confidence}") |
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# Multilingual analysis |
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multilingual_result = analyzer.analyze_sentiment_multilingual( |
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"¡Me encanta este chatbot! Es increíble!", # Spanish |
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enable_cross_language=True |
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) |
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print(f"Language: {analyzer.get_language_name(multilingual_result.detected_language)}") |
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print(f"Sentiment: {multilingual_result.sentiment}") |
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``` |
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## Installation |
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```bash |
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pip install sentilens-ai |
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python -m spacy download en_core_web_sm |
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pip install langdetect |
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``` |
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## Model Architecture |
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SentilensAI uses an ensemble approach combining: |
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1. **Traditional ML Models**: Random Forest, SVM, XGBoost, Neural Networks |
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2. **Deep Learning Models**: BERT, RoBERTa, DistilBERT, Twitter-RoBERTa |
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3. **LangChain Integration**: GPT-3.5, GPT-4, Claude, Custom LLMs |
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4. **Multilingual Models**: Language-specific transformer models |
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## Training Data |
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The model was trained on a diverse dataset including: |
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- Customer service conversations |
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- Social media interactions |
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- Product reviews and feedback |
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- Multilingual text samples |
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- Chatbot conversation logs |
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## Evaluation |
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- **Cross-Validation**: 5-fold cross-validation |
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- **Metrics**: Accuracy, Precision, Recall, F1-Score, ROC AUC |
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- **Languages**: English (94.2%), Spanish (92.8%), Chinese (91.5%) |
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- **Model Agreement**: 82%+ consensus across models |
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## Limitations |
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- Requires internet connection for LangChain LLM integration |
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- Model loading time on first use |
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- Memory requirements for deep learning models |
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- Language detection accuracy varies by text length |
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## Citation |
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```bibtex |
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@software{sentilensai2024, |
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title={SentilensAI: Advanced Sentiment Analysis for AI Chatbots}, |
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author={Kernelseed}, |
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year={2024}, |
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url={https://github.com/kernelseed/sentilens-ai} |
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} |
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``` |
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## License |
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This model is licensed under the MIT License. See the LICENSE file for details. |
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## Contact |
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- GitHub: https://github.com/kernelseed/sentilens-ai |
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- Email: [email protected] |
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- Documentation: https://github.com/kernelseed/sentilens-ai/wiki |
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--- |
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*Built with ❤️ for the AI community* |
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