A hierarchical neural model for target‐based sentiment analysis
Ke Chen, Wende Ke · Concurrency and Computation Practice and Experience · 2021
Abstract A convolutional neural network‐regional long Short‐Term memory (CNN‐RLSTM) is proposed, which is a convolutional neural network‐regional long short‐term memory (CNN‐RLSTM) that combines CNN and regional LSTM. The model can effectively distinguish the affective polarity of different targets through a regional LSTM while reducing the training time of the model. In addition, the model can retain the sentiment information of the whole sentence through a CNN network at the sentence level. Experimental results on different data sets show that the CNN‐RLSTM model is better than the traditional model and the deep network model.