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Virtual Agent for Real-Time Motivational Interviewing by Integrating Adaptive Nonverbal Behavior and Language Models
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Multimodal Sentiment and Emotion Analysis Framework for Personalized Health Coaching Messages: Proof-of-Concept

Muhammad Aiman Md Zuki1, Nazlena Mohamad Ali1, Jun Kit Chaw1

  • 1Institute of Visual Informatics (IVI), Universiti Kebangsaan Malaysia, Jalan Tun Ismail Ali, Bandar Baru Bangi, Selangor, 43600, Malaysia, 60 0389272402.

JMIR Formative Research
|April 21, 2026
PubMed
Summary
This summary is machine-generated.

Generative Pretrained Transformer-2 (GPT-2) shows promise in creating persuasive health messages, with transformer models outperforming traditional sentiment analysis tools but requiring more resources. Emotion detection in health communication is complex, with varying accuracy across different emotions.

Keywords:
emotion detectionhealth coachingnatural language processingpersuasive communicationsentiment analysis

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Area of Science:

  • Natural Language Processing
  • Health Communication
  • Computational Linguistics

Background:

  • Text generation in healthcare communication has advanced via Generative Adversarial Networks (GANs) and transformer architectures like Generative Pretrained Transformer-2 (GPT-2).
  • GPT-2 leverages extensive pretraining and self-attention for contextually appropriate text generation, enhancing persuasive health messages through personalized support strategies.
  • These advancements aim to improve health interventions by generating tailored communication for patients.

Purpose of the Study:

  • To utilize GPT-2 for generating persuasive health communication texts.
  • To compare the performance of sentiment analysis and emotion detection models on GPT-2 generated health messages.
  • To evaluate different natural language processing (NLP) tools for analyzing persuasive health communication.

Main Methods:

  • Combined sentiment analysis tools (VADER, TextBlob) with emotion detection methods (Text2Emotion, NRCLex).
  • Analyzed health coaching messages categorized by persuasive types: reminder, reward, suggestion, and praise.
  • Utilized RoBERTa-sentiment for comparison against lexicon-based models.

Main Results:

  • RoBERTa-sentiment achieved 88% accuracy, outperforming VADER (69%) and TextBlob (57%) in sentiment analysis.
  • Emotion detection analysis revealed a high prevalence of "joy" and "happy" (93.69% positive skew).
  • Transformer models offer high accuracy, while lexicon-based models like VADER provide a balance for real-time systems; emotion detection accuracy varied, with some emotions performing well and others showing mixed results.

Conclusions:

  • Current NLP tools have limitations and capabilities for analyzing persuasive health messages.
  • This study establishes a framework for multimodal sentiment and emotion analysis using synthetically generated data.
  • Findings require validation with real-world data before clinical application in health coaching.