A New Text Sentiment Analysis Method Based on Chinese Morphological Features and HowNet

Abstract

Abstract Traditional deep learning methods have two problems when using vectorized words as input. One is that they only consider the overall semantic information of the vocabulary, but ignore the morphological features of the Chinese vocabulary and the prior knowledge of the Chinese external knowledge base. Second, the word vector corresponding to each word will be limited to a single word vector training model. Aiming at these problems, we propose a double-channel convolutional neural network model based on Chinese morphological features and HowNet. First, the cw2vec model and the SAT model (Sememe Attention over Target Model) are used to train the word vectors. Second, the two different word vectors are used as the input of the two channels of the model. Finally, the convolutional neural network are used to extract the characteristics of the two channels to complete the sentiment analysis task. The comparative experimental results on the two data sets show that the proposed model achieves significantly better classification performance than traditional sentiment analysis methods.