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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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Hierarchical Network with Label Embedding for Contextual Emotion Recognition.

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  • 1Faculty of Engineering, Tokushima University, Tokushima 770-8506, Japan.

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Summary
This summary is machine-generated.

This study introduces a novel hierarchical model for contextual emotion recognition, improving accuracy by considering emotional relationships within text. The proposed method achieves satisfying performance on a Chinese emotional corpus.

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Area of Science:

  • Natural Language Processing
  • Artificial Intelligence
  • Computational Linguistics

Background:

  • Emotion recognition is crucial for applications like mental health monitoring and emotional management.
  • Traditional text classification methods often overlook the complex relationships between emotions in text.

Purpose of the Study:

  • To propose a novel hierarchical model with label embedding for enhanced contextual emotion recognition.
  • To address the non-negligible relations of emotions expressed in textual data.

Main Methods:

  • A hierarchical model is employed to learn emotional representations from contextual information.
  • A label embedding matrix is trained via joint learning to incorporate emotion correlation into predictions.

Main Results:

  • The proposed approach demonstrates satisfying performance in textual emotion recognition.
  • Experimental validation was conducted on the Chinese emotional corpus RenCECps.

Conclusions:

  • The hierarchical model with label embedding effectively captures contextual and relational aspects of emotion in text.
  • This method offers a promising advancement for accurate textual emotion recognition.