Journal of Information Processing Systems

Korea Information Processing Society (한국정보처리학회)
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Burmese Sentiment Analysis Based on Transfer Learning

  • Mao, Cunli (College of Information Engineering and Automation & Yunnan Key Laboratory of Artificial Intelligence, Kunming University of Science and Technology) ;
  • Man, Zhibo (College of Information Engineering and Automation & Yunnan Key Laboratory of Artificial Intelligence, Kunming University of Science and Technology) ;
  • Yu, Zhengtao (College of Information Engineering and Automation & Yunnan Key Laboratory of Artificial Intelligence, Kunming University of Science and Technology) ;
  • Wu, Xia (College of Information Engineering and Automation & Yunnan Key Laboratory of Artificial Intelligence, Kunming University of Science and Technology) ;
  • Liang, Haoyuan (College of Information Engineering and Automation & Yunnan Key Laboratory of Artificial Intelligence, Kunming University of Science and Technology)
  • Received : 2020.11.19
  • Accepted : 2022.03.25
  • Published : 2022.08.31
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Abstract

Using a rich resource language to classify sentiments in a language with few resources is a popular subject of research in natural language processing. Burmese is a low-resource language. In light of the scarcity of labeled training data for sentiment classification in Burmese, in this study, we propose a method of transfer learning for sentiment analysis of a language that uses the feature transfer technique on sentiments in English. This method generates a cross-language word-embedding representation of Burmese vocabulary to map Burmese text to the semantic space of English text. A model to classify sentiments in English is then pre-trained using a convolutional neural network and an attention mechanism, where the network shares the model for sentiment analysis of English. The parameters of the network layer are used to learn the cross-language features of the sentiments, which are then transferred to the model to classify sentiments in Burmese. Finally, the model was tuned using the labeled Burmese data. The results of the experiments show that the proposed method can significantly improve the classification of sentiments in Burmese compared to a model trained using only a Burmese corpus.

Keywords

Acknowledgement

This work was supported by the Key Program of the National Natural Science Foundation of China (No. U21B2027 and 61732005); the National Natural Science Foundation of China (No. 61866019, 62166023, and 61972186), Yunnan Provincial Major Science and Technology Special Plan Projects (No. 202103AA080015 and 202002AD080001), the Program for Applied & Basic Research of Yunnan Province in Key Areas (No. 2019FA023), and the Candidates of the Young and Middle Aged Academic and Technical Leaders of Yunnan Province (No. 2019HB006).

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