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Related Concept Videos

Stereotype Content Model02:16

Stereotype Content Model

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The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence...
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Some researchers suggest that altruism operates on empathy. Empathy is the capacity to understand another person’s perspective, to feel what he or she feels. An empathetic person makes an emotional connection with others and feels compelled to help (Batson, 1991). Empathy can be expressed in several ways, including cognitive, affective, and motor. 
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Virtual Agent for Real-Time Motivational Interviewing by Integrating Adaptive Nonverbal Behavior and Language Models
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A machine learning based empathy mapping framework for enhancing user experience through app review analysis.

Faryal Ishfaq1, Safdar Nawaz Khan Marwat2,3, Waseem Ullah Khan2,3

  • 1Department of Computer Science, University of Peshawar, Peshawar, Pakistan.

Scientific Reports
|December 2, 2025
PubMed
Summary
This summary is machine-generated.

This study automates user experience (UX) empathy mapping using Bidirectional Encoder Representations from Transformers (BERT) and Latent Dirichlet Allocation (LDA) on app reviews. This data-driven approach enhances scalability and efficiency in UX design research.

Keywords:
App review miningBERTDeep learningEmpathy mappingNatural language processing (NLP)Sentiment analysisSoftware engineeringText classificationTopic modelingUser experience

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Area of Science:

  • Human-Computer Interaction
  • Software Engineering
  • Natural Language Processing

Background:

  • User experience (UX) is critical for software effectiveness, impacting user engagement and satisfaction.
  • Empathy mapping, a design thinking technique, aids in understanding user perceptions but is traditionally manual, time-consuming, and costly.
  • Existing methods limit the scalability of UX research and design processes.

Purpose of the Study:

  • To develop an automated process for empathy mapping using user-posted app reviews.
  • To enhance the efficiency and scalability of UX design and research.
  • To leverage natural language processing for data-driven UX insights.

Main Methods:

  • Sentiment analysis using Bidirectional Encoder Representations from Transformers (BERT) to classify reviews into positive (gains) and negative (pains).
  • Topic modeling with Latent Dirichlet Allocation (LDA) to identify user preferences and key themes from reviews.
  • Automated analysis of app reviews to replace traditional manual empathy mapping techniques.

Main Results:

  • The proposed BERT model achieved high accuracy in sentiment classification, with training binary accuracy reaching 98.61% (precision 97.82%, F1 98.62%, recall 99.42%).
  • Validation accuracy reached 92.58% (F1 92.59%, precision 92.43%, recall 92.75%), demonstrating robust performance.
  • The automated method significantly improves upon traditional manual empathy mapping in terms of efficiency and scalability.

Conclusions:

  • The automated empathy mapping process offers a scalable and efficient alternative to traditional methods.
  • This data-driven approach provides valuable insights for UX design teams and developers.
  • The model facilitates prompt user feedback, enabling continuous improvement of software applications.