Text Sentiment Analysis Based on Parallel Recursive Constituency Tree-LSTM
Yongheng Wang, Xiaozan Zhang, Xiangge Wang, Rong Zhu, Zengwang Wang, Lijie Liu · 2019
With the rapid development of mobile Internet, online platforms such as social entertainment and e-commerce have brought a large number of unstructured texts containing sentiment polarity. Sentiment analysis of these texts can be used for public opinion analysis, product review and other tasks. Recent researches show great interest and success in using deep learning methods for sentiment analysis tasks. Among these contributions, Constituency Tree-LSTM has achieved good results because of its superior ability and tree structure to preserve sequence information over time. However, the model calculates the sentiment label of the node based mainly on its vector representation, while missing the sentiment information of its children. This paper proposes a parallel recursive Constituency Tree-LSTM (PRCT-LSTM), which combines recursive neural network with Constituency Tree-LSTM to achieve better performance. The node sentiment information propagates to the global evaluation through recursive neural networks. Therefore, the sentiment information of each node in the PRCT-LSTM structure consists of sentiment information generated by Constituency Tree-LSTM and the recursive model. In this way, we take more full usage of sentiment information. Experimental results manifest that the proposed model achieves a higher performance than the Constituency Tree-LSTM.