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Revolutionizing Chinese sentiment analysis: A knowledge-driven approach with multi-granularity semantic features.

Ping He1

  • 1Changsha Institute of Technology, Changsha, China.

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|July 15, 2025
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Summary
This summary is machine-generated.

This study enhances Chinese text sentiment analysis by integrating emotional knowledge graphs and linguistic features. The novel approach significantly improves sentiment detection accuracy on benchmark datasets.

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Area of Science:

  • Natural Language Processing
  • Computational Linguistics
  • Artificial Intelligence

Background:

  • Chinese text sentiment analysis research has advanced, but gaps remain regarding cross-lingual differences, domain knowledge, and task-specific needs.
  • Existing methods often overlook the unique linguistic characteristics of Chinese text, limiting sentiment analysis performance.
  • Practical applications require more robust and accurate sentiment detection tailored to the nuances of Chinese language.

Purpose of the Study:

  • To address the limitations in current Chinese text sentiment analysis.
  • To propose a novel method that deeply integrates emotional knowledge and linguistic features.
  • To enhance the accuracy and effectiveness of sentiment detection for Chinese texts.

Main Methods:

  • Proposed a method integrating knowledge vectors from emotional knowledge triplets (using TransE) with BiGRU and attention mechanism feature vectors.
  • Introduced radical and emotional part-of-speech features, leveraging character and word characteristics.
  • Developed a collaborative approach integrating multi-granularity features: characters, words, radicals, and parts of speech.

Main Results:

  • Achieved an F1-score of 89.23% on the Douban Film Review dataset.
  • Achieved an F1-score of 84.84% on the NLPECC dataset.
  • Demonstrated significant improvement in sentiment detection accuracy by leveraging emotional insights and linguistic elements.

Conclusions:

  • The proposed method effectively leverages emotional knowledge and linguistic nuances for superior Chinese sentiment analysis.
  • The integration of multi-granularity features significantly bolsters the accuracy of sentiment detection.
  • The approach shows strong efficacy and potential for practical applications in Chinese sentiment analysis.