Research on Text Sentiment Analysis Based on Sentiment Dictionary and Deep Learning
The sentiment analysis is a crucial part of natural language processing.At present,the deep learning model of word embedding combined with neural network has achieved good results in the research of Chinese text sentiment analysis.However,the emotional information of the current word will be lost when using word embedding model only as text representation to learn the mod-el.In this work,a text sentiment analysis model SABLSTM is proposed,which is based on sentiment dictionary combined with bidi-rectional long short-term memory network and attention mechanism.The precision of this model on the hotel dataset is 93.17%,and it has achieved a great improvement of 1.56%compared with the model integrated the bidirectional long short-term memory network structure with attention mechanism.Therefore,the model training with sentient lexicon as prior knowledge can improve the effect of Chinese text sentiment analysis task.
sentiment analysissentiment lexiconattention mechanismbidirectional long short-term memory network
国家科技期刊平台
NSTL
万方数据
