SayPro emotional tone detection via validated review sampling

Select and Prepare a Validation Sample

• Randomly select a representative sample of feedback entries (e.g., 500–1,000) covering all service lines and demographics.
• Have human annotators (trained reviewers) manually label the emotional tone for each entry.
• Use a consistent, clear annotation guideline for emotions (e.g., Joy, Sadness, Anger, Fear, Neutral).

2. Develop or Choose Your Detection Model

• Use a proven NLP model or GPT prompt specifically fine-tuned or designed for emotion detection.
• Optionally, fine-tune on a labeled training dataset relevant to SayPro’s domain and language style.

3. Run Automated Emotional Tone Detection

• Apply your model to the same validation sample to predict emotional tones.

4. Compare & Calculate Accuracy

• Compare model predictions against the human-annotated labels.
• Calculate accuracy as: Accuracy=Number of Correct PredictionsTotal Number of Samples×100%\text{Accuracy} = \frac{\text{Number of Correct Predictions}}{\text{Total Number of Samples}} \times 100\%Accuracy=Total Number of SamplesNumber of Correct Predictions​×100%
• Target ≥ 85%.

5. Error Analysis

• Identify where the model misclassifies emotions.
• Check for ambiguous, sarcastic, or mixed-tone feedback.
• Note any language, slang, or domain-specific terms causing errors.

6. Iterate and Improve

• Adjust model parameters or retrain on expanded datasets including misclassified samples.
• Enhance annotation guidelines or use multiple annotators to improve label quality.
• Experiment with ensemble models or hybrid human+AI review for borderline cases.

7. Ongoing Quality Control

• Periodically validate new samples (e.g., monthly).
• Update model with new data and feedback patterns to maintain accuracy.