Interactive Attention-Based Convolutional GRU for Aspect Level Sentiment Analysis - Lisha Chen; Tianrui Li; Huaishao Luo; Chengfeng Yin (Human-Centric Intelligent Systems, 2021)
Abstract Aspect level sentiment analysis aims at identifying sentiment polarity towards specific aspect terms in a given sentence. Most methods based on deep learning integrate Recurrent Neural Network (RNN) and its variants with the attention mechanism to model the influence of different context words on sentiment polarity. In recent research, Convolutional Neural Network (CNN) and gating mechanism are introduced to obtain complex semantic representation. However, existing methods have not realized the importance of sufficiently combining the sequence modeling ability of RNN with the high-dimensional feature extraction ability of CNN. Targeting this problem, we propose a novel solution named Interactive Attention-based Convolutional Bidirectional Gated Recurrent Unit (IAC-GRU). IAC-GRU not only incorporates the sequence feature extracted by Bi-GRU into CNN to accurately predict the sentiment polarity, but also models the target and the context words separately and learns mutual influence between them. Additionally, we also incorporate the position information and Part-of-Speech (POS) information as prior knowledge into the embedding layer. The experimental results on SemEval2014 datasets show the effectiveness of our proposed model.