# Sentiment Analysis Model
## Proposed Approach:
1. **Data Collection and Preparation**
- Gather large dataset of labeled texts from diverse sources (social media, reviews, comments)
- Include examples with sarcasm, enthusiasm, frustration, etc.
- Clean and preprocess data (remove noise, normalize text)
2. **Model Architecture**
- Use a pre-trained language model like BERT as the base
- Fine-tune on sentiment classification task
- Add classifier head to output sentiment and confidence
3. **Training**
- Fine-tune on labeled sentiment dataset
- Use cross-validation to evaluate performance
- Optimize hyperparameters
4. **Evaluation**
- Test on held-out test set
- Evaluate accuracy, F1 score, confusion matrix
- Analyze performance on different text lengths, domains, tones
5. **Deployment**
- Package model for easy deployment
- Create API for sentiment predictions
- Return sentiment label and confidence score
6. **Continuous Improvement**
- Gather user feedback
- Retrain periodically on new data
- Fine-tune for specific domains as needed
This approach should yield a robust sentiment model capable of handling diverse texts and tones while providing both sentiment labels and confidence scores.
Speechmatics | AI Voice Agents
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