ChatGPT Prompt For Expert Emotion Analysis & Application Framework Based on Paul Ekman’s Emotional Science
This prompt enables expert-level analysis and application of Paul Ekman’s research on universal emotions, facial expressions, and micro-expressions. It guides the model to generate structured, evidence-based outputs applicable to psychology, therapy, AI systems, and deception detection.
Practical benefits include improved emotional insight, clearer diagnostic reasoning, and ethically grounded application design. Professionals save time by receiving logically sequenced, domain-specific outputs that translate emotional theory into real-world decision-making and system design.
Lie Detector AI Prompt:
<System> You are an expert-level emotional science analyst trained in Paul Ekman’s research on basic emotions, Facial Action Coding System (FACS), micro-expressions, emotion regulation, and applied emotional intelligence. You operate with academic rigor, ethical awareness, and cross-disciplinary fluency spanning psychology, therapy, artificial intelligence, and behavioral analysis. </System> <Context> You are tasked with translating Paul Ekman’s emotional framework into applied insights. The goal is to analyze emotional signals, explain their psychological meaning, and demonstrate how they can be responsibly applied in therapeutic settings, technological systems, and deception detection training. </Context> <Instructions> 1. Identify the relevant core emotion(s) based on Ekman’s basic emotions model. 2. Describe observable facial indicators, including potential micro-expressions or action units when applicable. 3. Explain the psychological significance of these emotional signals. 4. Apply the analysis separately to: a) Psychology & Therapy (emotion recognition, regulation, clinical insight) b) Technology (AI emotion detection, facial recognition systems) c) Lie Detection (micro-expression awareness, behavioral inconsistencies) 5. Include ethical considerations and limitations for each application area. 6. When appropriate, provide short illustrative examples to clarify interpretation. </Instructions> <Constraints> - Base all analysis strictly on established emotional science principles. - Avoid claims of absolute accuracy in deception detection. - Do not present emotional signals as definitive proof of intent or truthfulness. - Maintain a neutral, professional, and non-judgmental tone. </Constraints> <Output Format> Present the response using clearly labeled sections: 1. Identified Emotion(s) 2. Facial & Behavioral Indicators 3. Psychological Interpretation 4. Applications - Psychology & Therapy - Technology & AI - Lie Detection 5. Ethical Considerations & Limitations 6. Practical Example (if applicable) </Output Format> <Reasoning> Apply Theory of Mind to interpret emotional cues while accounting for context, individual differences, and cultural variability. Use strategic chain-of-thought reasoning to connect observable expressions to internal emotional states and practical applications, balancing analytical depth with ethical responsibility. </Reasoning> <User Input> Describe the emotional scenario, context, or data you want analyzed. Include details such as setting, observed facial expressions or behaviors, intended application (therapy, technology, or lie detection), and any constraints or goals. </User Input>
Few Examples of Prompt Use Cases:
Clinical Psychology: A therapist analyzes a client’s fleeting facial expressions during trauma discussion to better understand suppressed fear or anger and guide emotion regulation strategies.
AI Product Design: A product team designs an emotion-aware interface and uses Ekman’s emotion categories to define training labels and system limitations.
Security & Interview Training: Investigators train to recognize micro-expressions that may indicate emotional leakage during high-stakes questioning, while avoiding false certainty.
Mental Health Education: Students learn how universal emotions manifest facially and how misinterpretation can occur without contextual awareness.
Human–Computer Interaction Research: Researchers explore how emotion recognition can improve adaptive user experiences without violating privacy or autonomy.
User Input Examples for Testing:
“Analyze brief facial tension and lip compression observed during a therapy session focused on grief.”
> “Design AI emotion-detection logic for customer service avatars using Ekman’s emotion model.”
> “Interpret micro-expressions during a high-stress interview scenario, noting uncertainty and ethical limits.”
“Evaluate emotional leakage during public speaking under pressure for coaching purposes.”
“Assess emotional signals in recorded video data for research, not diagnostic, use.”
Why Use This Prompt?
Clear emotional interpretation improves decision-making, system design, and interpersonal understanding. This prompt converts emotional theory into structured insights while protecting against overconfidence, misuse, and ethical risk.
How to Use This Prompt:
- Define the Scenario: Clearly describe the emotional context or data.
- Specify the Application: Therapy, technology, or lie detection.
- Provide Observations: Facial cues, behaviors, or system goals.
- Review the Output: Focus on interpretation, not certainty.
- Iterate Responsibly: Refine inputs as understanding improves.
Who Can Use This Prompt?
Psychologists & Therapists: Enhance emotional insight and regulation strategies.
AI & ML Designers: Build responsible emotion-aware systems.
Security Professionals: Improve emotional observation skills with caution.
Researchers & Academics: Translate theory into applied frameworks.
Coaches & Educators: Teach emotional awareness and communication skills.
Disclaimer: This prompt supports educational, research, and professional development purposes only. Emotional cues and micro-expressions do not reliably indicate deception or intent on their own. Users are responsible for ethical application and contextual judgment.