Cascade architecture with rhetoric long short-term memory for complex sentence sentiment analysis

Abstract

In a sentiment analysis task, it is essential to differentiate the various and sometimes even contradictory emotions in each segment and to judge the underlying true emotion of the whole sentence. Rhetorical structure theory hierarchically structures the relationships between segments and describes the effects of relations. This study proposes a flexible cascade architecture: the lower unit divides the sentence into segments and obtains their distributed representation vector; the upper rhetoric-based long short-term memory unit aggregates the information of every segment and applies the concrete hierarchical relation information to perform sentiment analysis. Auxiliary techniques, namely, data augmentation and relation clustering, are also proposed for preventing overfitting. The experiment results prove that the proposed cascade architecture and auxiliary techniques improve the traditional approaches in most cases, which shows 3.17% accuracy growth in the fine-grained classification and 1.41% in the binary tasks at most. Furthermore, the cascade architecture is flexible enough, which could be easily extended by combining the Rhetoric-LSTM unit with the state-of-the-art classification or pre-trained models if necessary.