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Emotional labeling is a cognitive process that involves identifying and naming one's emotions, such as anger, fear, happiness, or sadness. It allows individuals to recognize and express their internal emotional states, a critical aspect of emotional regulation and communication. Labeling emotions requires more than mere recognition; it also involves drawing upon memory and contextual cues to understand the current situation and apply a corresponding emotional label. For instance, feeling...
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Related Experiment Video

Updated: Sep 23, 2025

Exploring the Use of Isolated Expressions and Film Clips to Evaluate Emotion Recognition by People with Traumatic Brain Injury
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TERMS: textual emotion recognition in multidimensional space.

Yusra Ghafoor1, Shi Jinping2, Fernando H Calderon1

  • 1Social Networks and Human-Centered Computing, Taiwan International Graduate Program, Institute of Information Science, Academia Sinica. Institute of Information Systems and Applications, National Tsing Hua University, Hsinchu City, Taiwan.

Applied Intelligence (Dordrecht, Netherlands)
|May 17, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a new model for recognizing emotions in microblog text. The TERMS model accurately captures subjective feelings and context, outperforming existing methods in emotion classification.

Keywords:
Emotion distributionEmotion recognitionGaussian mixture modelSubjectivityText classificationValence-Arousal

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Area of Science:

  • Natural Language Processing
  • Affective Computing
  • Computational Linguistics

Background:

  • Microblogs generate extensive data reflecting user emotions.
  • Recognizing emotions in subjective, context-dependent text is challenging.

Purpose of the Study:

  • To propose an intelligent probabilistic model for textual emotion recognition in multidimensional space (TERMS).
  • To capture subjective emotional boundaries and contextual information for robust emotion recognition.

Main Methods:

  • TERMS employs soft assignment using a Gaussian Mixture Model (GMM) for varying emotional perceptions.
  • Integrates a probabilistic emotion classifier to leverage contextual and linguistic text information.

Main Results:

  • TERMS demonstrated superior performance over baseline and state-of-the-art models in distinguishability, prediction, and classification.
  • The model provides insights into emotion classes, annotation patterns, and application scenarios.

Conclusions:

  • The proposed TERMS model offers accurate and robust textual emotion recognition.
  • TERMS enhances understanding of emotion expression in microblogging data.