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Emotion Analysis Method of Teaching Evaluation Texts Based on Deep Learning in Big Data Environment.

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This study introduces a deep learning model for precise emotion analysis in teaching evaluations. The method enhances educational quality by accurately identifying emotions in complex texts.

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Area of Science:

  • Natural Language Processing
  • Artificial Intelligence
  • Educational Technology

Background:

  • Effective emotion analysis of teaching evaluations is crucial for improving educational quality.
  • Existing methods may lack the precision needed for complex textual data.

Purpose of the Study:

  • To propose a novel deep learning-based method for accurate emotion recognition and analysis in teaching evaluation texts.
  • To enhance the precision and reliability of emotion analysis in educational contexts.

Main Methods:

  • Utilized the LTP tool for effective preprocessing of teaching evaluation text datasets.
  • Developed an emotion analysis model using a bidirectional long short-term memory (BiLSTM) network.
  • Integrated an attention interaction mechanism module to focus on critical information and extract deeper emotional features.

Main Results:

  • The proposed method achieved an accuracy of 0.9123 and a precision of 0.8214 in emotion recognition.
  • Experimental results demonstrate the model's effectiveness in analyzing complex teaching evaluation texts.

Conclusions:

  • The developed deep learning model, incorporating BiLSTM and attention mechanisms, significantly improves emotion recognition accuracy.
  • This approach meets the demands for precise emotion analysis in educational settings, aiding teacher development.