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The character vocabulary can be very large in non-alphabetic languages suchas Chinese and Japanese, which makes neural network models huge to process suchlanguages. We explored a model for sentiment classification that takes theembeddings of the radicals of the Chinese characters, i.e, hanzi of Chinese andkanji of Japanese. Our model is composed of a CNN word feature encoder and abi-directional RNN document feature encoder. The results achieved are on parwith the character embedding-based models, and close to the state-of-the-artword embedding-based models, with 90% smaller vocabulary, and at least 13% and80% fewer parameters than the character embedding-based models and wordembedding-based models respectively. The results suggest that the radicalembedding-based approach is cost-effective for machine learning on Chinese andJapanese.

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