A Deep Network Model for Specific Target Sentiment Analysis

The Long Short Term Memory (LSTM) network based on attention mechanism generally takes a lot of time during the training process, and only uses sentences as a network input, which is difficult to effectively distinguish the different polarities of different targets in the same sentence. To address this problem, this paper proposes a deep neural model of combining Convolutional Neural Network (CNN) and Regional LSTM (CNN-RLSTM). By segmenting the region according to the specific target through the regional LSTM, the feature information of different targets can be effectively distinguished while retaining the specific emotional information of the specific target, and the emotional information of the entire sentence is retained by the CNN. Experimental results show that, the CNN-RLSTM model can effectively identify the emotional polarity of different targets, and the model training time is shorter than the traditional network model.