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Area of Science:

  • Natural Language Processing
  • Computational Linguistics
  • Social Media Analytics

Background:

  • Globalization and digitalization increase the challenge of cross-lingual information dissemination.
  • Existing public opinion analysis systems face bottlenecks in multilingual scenarios.

Purpose of the Study:

  • To propose a novel cross-lingual sentiment analysis framework, CLAS-Net.
  • To address limitations in current multilingual public opinion analysis.

Main Methods:

  • Developed CLAS-Net, integrating XLM-RoBERTa for cross-lingual contrastive learning.
  • Utilized BiLSTM-Attention for precise sentiment feature extraction.

Main Results:

  • Achieved 92% accuracy in English and 89% in Portuguese (monolingual tasks).
  • Demonstrated 83% accuracy in challenging multilingual settings.
  • Both results represent a 29 percentage point improvement over baseline models.

Conclusions:

  • CLAS-Net shows strong adaptability and practical value for real-world social media and news data.
  • Provides reliable technical support for cross-lingual public opinion monitoring and analysis.
  • Highlights the effectiveness of combining cross-lingual contrastive learning with attention-based LSTMs.