Multi-entity sentiment analysis using self-attention based hierarchical dilated convolutional neural network

Abstract

In this paper, a self-attention based hierarchical dilated convolutional neural network (SA-HDCNN) is proposed for multi-entity sentiment analysis (MESA), in which the task is directly transformed into a sequence labeling problem avoiding decomposition and is also suitable for parallel computing. Specifically, SA-HDCNN is mainly composed of encoding, feature extraction and decoding modules. The encoding module is to map the input sentence into a word embedding matrix, which contains both semantic and sentiment information. Next, the feature extraction module is to learn multi-scale local features and inter-word global dependencies of the encoded sentence through HDCNN architecture and self-attention mechanism, respectively. Afterwards, the decoding module is to output the sequence of tags, thereby completing the automatic recognition of multiple target entities and their corresponding sentiment polarities. Finally, experimental results demonstrate that, without any domain specific features or prior information, our method surpasses the existing state-of-the-art methods, implying its feasibility and wide applicability.