Research on sentiment classification of Chinese short text

Abstract

Sentiment classification can be widely used in various fields because of the value it has gained, and has received extensive attention in recent years. No matter the traditional methods based on language rules, machine statistical learning, or the current popular deep learning have achieved great success in this respect, but up to now, because of the particularity of Chinese, there are still few methods for Chinese text sentiment classification. Moreover, the traditional method is time-consuming and labor-consuming, with poor versatility. The neural network method saves resources and is generally applicable, but the application of language knowledge is not sufficient. For this reason, we propose a method combining traditional method with neural network, which combines sentiment lexicon, attention mechanism and stacked BILSTM. Word vectors are generated using word2vec, and the context semantics are captured multiple times by stacked BILSTM to get richer semantic information, for sentiment classification, sentiment words often have more influence on the result of classification, so we use sentiment lexicon which contains rich sentiment word to monitor the attention mechanism, and make the attention model pay more attention to sentiment words. Combining the advantages of traditional methods and neural networks, we use the proposed method to do the sentiment binary classification experiments on two data sets. The experimental results show that our proposed method is superior to the previous method.