Radical-level Ideograph Encoder for RNN-based Sentiment Analysis of Chinese and Japanese

Yuanzhi Ke, Masafumi Hagiwara

Research output: Contribution to journalConference articlepeer-review

11 Citations (Scopus)


The character vocabulary can be very large in non-alphabetic languages such as Chinese and Japanese, which makes neural network models huge to process such languages. We explored a model for sentiment classification that takes the embeddings of the radicals of the Chinese characters, i.e, hanzi of Chinese and kanji of Japanese. Our model is composed of a CNN word feature encoder and a bi-directional RNN document feature encoder. The results achieved are on par with the character embedding-based models, and close to the state-of-the-art word embedding-based models, with 90% smaller vocabulary, and at least 13% and 80% fewer parameters than the character embedding-based models and word embedding-based models respectively. The results suggest that the radical embedding-based approach is cost-effective for machine learning on Chinese and Japanese.

Original languageEnglish
Pages (from-to)561-573
Number of pages13
JournalJournal of Machine Learning Research
Publication statusPublished - 2017
Event9th Asian Conference on Machine Learning, ACML 2017 - Seoul, Korea, Republic of
Duration: 2017 Nov 152017 Nov 17


  • Natural Language Processing
  • Sentiment Analysis

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Statistics and Probability
  • Artificial Intelligence


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