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Chinese text dual attention network for aspect-level sentiment classification.

Xinjie Sun1,2, Zhifang Liu1, Hui Li1

  • 1Institute of Computer Science, Liupanshui Normal University, Liupanshui, Guizhou, China.

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A new dual attention network effectively handles Chinese text for aspect-level sentiment recognition by analyzing syntactic dependencies and context. This method improves accuracy in identifying sentiment trends and opinion extraction.

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Area of Science:

  • Natural Language Processing
  • Artificial Intelligence
  • Computational Linguistics

Background:

  • Chinese text often lacks explicit subjects, complicating word dependency analysis, particularly for aspect-level sentiment recognition.
  • Existing models struggle with the nuanced and context-dependent nature of sentiment expression in Chinese.
  • Accurate aspect-level sentiment recognition is crucial for understanding user opinions and feedback.

Purpose of the Study:

  • To propose a novel Chinese text dual attention network for enhanced aspect-level sentiment recognition.
  • To address the challenges posed by the loose sentence structure and lack of subjects in Chinese text.
  • To improve the accuracy and efficiency of sentiment analysis in Chinese reviews and experimental summaries.

Main Methods:

  • Developed a Chinese syntactic dependency analysis approach combined with a sentiment dictionary for precise aspect-level sentiment word extraction.
  • Employed Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory (BILSTM) with position coding to capture context-level features.
  • Implemented a two-level attention mechanism to extract fine-grained aspect-level sentiment information.

Main Results:

  • The proposed dual attention network achieved high accuracy rates of 0.9180, 0.9080, and 0.8380, outperforming ten advanced baseline models.
  • Experiments demonstrated the model's effectiveness in quickly and accurately extracting sentiment words, opinions, and classifying sentiment trends.
  • Ablation studies confirmed the significant contribution of each module within the proposed network.

Conclusions:

  • The Chinese text dual attention network offers a robust solution for aspect-level sentiment recognition in Chinese.
  • The integration of syntactic dependency, sentiment dictionaries, CNN-BILSTM, and a two-level attention mechanism significantly enhances performance.
  • This approach provides a more efficient and accurate method for analyzing sentiment in Chinese natural language data.