Aspect opinion routing network with interactive attention for aspect-based sentiment classification

Aspect-based sentiment classification task aims at discovering the sentiment polarity on a specific aspect term. Since a sentence often contains several sentiments for different aspects, it is very important to capture the correspondences between aspects and opinion words in a sentence. To this end, we propose an aspect opinion interactive attention routing network model, which adopts an interactive attention mechanism between specific aspect embeddings learned from graph convolutional network and opinion semantic embeddings learned from three bi-directional long short-term memory networks. Furthermore, dynamic routing is applied to the neural network so that its output values are constantly regulated during training to generate feature representations of specific aspects related to opinion word. Based on the above, our approach identifies opinion words feature information related to specific aspect words in context. Experimental results demonstrate that the model outperforms 16 existing methods in terms of accuracy and macro-average F1 score on most of the benchmark datasets.