A P-LSTM neural network for sentiment classification

Abstract

Neural network models have been demonstrated to be capable of achieving remarkable performance in sentiment classification. Convolutional neural network (CNN) and recurrent neural network (RNN) are two mainstream architectures for such modelling task. In this work, a novel model based on long short-term memory recurrent neural network (LSTM) called P-LSTM is proposed for sentiment classification. In P-LSTM, three-words phrase embedding is used instead of single word embedding as is often done. Besides, P-LSTM introduces the phrase factor mechanism which combines the feature vectors of the phrase embedding layer and the LSTM hidden layer to extract more exact information from the text. The experimental results show that the P-LSTM achieves excellent performance on the sentiment classification tasks.