A Neural Network-based Sentiment Analysis Scheme for Tang Poetry
Yongrui Tang, Xumei Wang, Peng Qi, Yan Sun · 2020
Poetry is a very popular literary form. Currently, its sentiment analysis is one of the hottest research trends. However, there are few relevant studies focusing on the sentiment analysis of ancient Chinese poetry, especially for Tang Poetry. In this paper, we propose a deep learning-based method to solve the above problem. Specifically, we combine Convolutional Neural Network and Gate Recurrent Unit to better extract the characteristics of Tang poetry. In addition, considering the special structural characteristics of Tang poetry, a multi-channel processing model is used to reshape the feature vector of sentences. Finally, in order to verify the rationality and superiority of the proposed methods, we construct a dataset by labeling more than 2500 representative Tang poems. The experimental results prove that our scheme has a higher accuracy rate, up to 64%, compared against three other competing methods.