基于 CRT 机制混合神经网络的特定目标情感分析.

The purpose of target-specific affective analysis is to predict the sentiment of a text from the perspective of different target words. The key is to assign appropriate affective words to a given target. When there are more than one affective word describing multiple target sentiments in a sentence, it may lead to the mismatch between the affective word and the target. This paper proposed a hybrid neural network based on CRT mechanism for target-specific sentiment analysis. The model used CNN layer to extract features from the word representation after BiLSTM transformation. It generated the specific target representation of the word by CRT component and saved the original context information from BiLSTM layer. Experiments on three open datasets show that the proposed model can significantly improve the accuracy and stability of target-specific affective analysis tasks compared with previous models. It is proved that the CRT mechanism can integrate the advantages of CNN and LSTM well, which is of great significance to the task of sentiment analysis for specific targets. [ABSTRACT FROM AUTHOR]