Radical-Based Hierarchical Embeddings for Chinese Sentiment Analysis at Sentence Level

Text representation in Chinese sentiment analysis is usually working at word or character level. In this paper, we prove that radical-level processing could greatly improve sentiment classification performance. In particular, we propose two types of Chinese radical-based hierarchical embeddings. The embeddings incorporate not only semantics at radical and character level, but also sentiment information. In the evaluation of our embeddings, we conduct Chinese sentiment analysis at sentence level on four different datasets. Experimental results validate our assumption that radical-level semantics and sentiments can contribute to sentence-level sentiment classification and demonstrate the superiority of our embeddings over classic textual features and popular word and character embeddings.