Tone Analysis classifies the emotional tone of responses into distinct categories to ensure appropriate emotional context.

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Emotion categories

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Available Emotion Categories

Neutral

Balanced and objective tone

Joy

Happiness and delight

Love

Affection and warmth

Fear

Anxiety and concern

Surprise

Astonishment and wonder

Sadness

Melancholy and grief

Anger

Frustration and rage

Annoyance

Irritation and displeasure

Confusion

Uncertainty and puzzlement

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Calculation method

Tone analysis is computed through a specialized process:
1

Model Architecture

The analysis system utilizes a Small Language Model (SLM) trained on a comprehensive combination of open-source and internal datasets to accurately classify emotional tones across multiple categories.
2

Performance Validation

The classification system demonstrates strong reliability with an 80% accuracy rate when evaluated against the GoEmotions validation dataset, a widely-used benchmark for emotion detection.

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Optimizing your AI system

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Managing Tone in Your System

When optimizing the emotional tone of your system, consider these approaches:
Define tone preferences: Set appropriate emotional tones for different contexts and user interactions.
Implement tone filters: Discourage undesirable emotional responses while promoting preferred tones.
Recognize and categorize the emotional tone of responses to align with user preferences and context, ensuring appropriate emotional engagement in AI interactions.