Is position important? deep multi-task learning for aspect-based sentiment analysis

Jie Zhou, Jimmy Xiangji Huang, Qinmin Vivian Hu, Liang He

Abstract: The position information of aspect is essential and useful for aspect-based sentiment analysis, while how to model the position of the aspect effectively during aspect-based sentiment analysis has not been well studied. Inspired by the intuition that the position prediction can help boost the performance of aspect-based sentiment analysis, we propose a D eep M ulti-T ask L earning (DMTL) model, which handles sentiment prediction (SP) and position prediction (PP) simultaneously. In particular, we first use a shared layer to learn the common features of the two tasks. Then, two task-specific layers are utilized to learn the features specific to the tasks and perform position prediction and sentiment prediction in parallel. Inspired by autoencoder structure, we design a position-aware attention and a deep bi-directional LSTM (DBi-LSTM) model for sentiment prediction and position prediction respectively to capture the position information better. Extensive experiments on four benchmark datasets show that our approach can effectively improve the performance of aspect-based sentiment analysis compared with the strong baselines.