Deep convRNN for sentiment parsing of Chinese microblogging texts
Jiajun Cheng, Sheng Zhang, Pei Li, Xin Zhang, Hui Wang · 2017
Recurrent Neural Networks (RNNs) are naturally applicable to sequential processing and have achieved outstanding performance in analyzing natural language. However, RNN-based sequence labeling methods may encounter some problems, such as word ambiguous and low-fidelity of word segmentation, in sentiment parsing of Chinese microblogging texts because it cannot well grasp local contextual information of words. Therefore, in this work, we propose a novel neural network architecture, named convRNN, for sentiment parsing of Chinese texts. The convRNN combines Convolutional Neural Network (CNN) and RNN to capture the local contextual feature and global sequence feature of words in a sentence. Experimental results demonstrate that extracting local contextual features of words with CNN improves the performance of RNN models. Furthermore, deep convRNNs achieve better performance than shallow models and outperform the RNN-based method substantially.