%0 Journal Article %A CHENG Yan %A YE Ziming %A WANG Mingwen %A ZHANG Qiang %A ZHANG Guanghe %T Chinese Text Sentiment Orientation Analysis Based on Convolution Neural Network and Hierarchical Attention Network %D 2019 %R %J Journal of Chinese Information Processing %P 133-142 %V 33 %N 1 %X Text sentiment orientation analysis is a fundamental problem in natural language processing. To further improve the deep learning based models used in this issue, this paper proposes a new model named C-HAN, i.e. Convolutional Neural Network-based and Hierarchical Attention Network-based Chinese Sentiment Classification Model. It utilizes a convolution layer to extract a sequence of higher-level phrase representations, which are then fed into a hierarchical attention network to obtain the final representations. On the Chinese sentiment analysis corpus, the character level C-HAN achieves a sentiment prediction accuracy of 92.34%, slightly better than the word level C-HAN yielding 91.96% accuracy. %U http://jcip.cipsc.org.cn/EN/abstract/article_2707.shtml