Integration of local position-POS awareness and global dense connection for ABSA

Aspect-based sentiment analysis (ABSA) is a key problem in text analysis. However, previous work ignores the fact that the joint effects of local and global features affect the classification accuracy. Therefore, an ABSA model based on local position-part of speech (POS) awareness and global dense connection (LPP-GDC) is proposed to fully grasp the information from both local and global features concurrently. First, the BERT pre-trained model is used to obtain the word vectors. Second, position-POS awareness mechanism is designed to focus on words of important POS in the local context. Then, the multi-head self-attention mechanism and a densely connected graph convolutional network are constructed to capture the global information. Finally, the results are obtained by the dynamic feature fusion method. Experiments on three public datasets show that LPP-GDC obtains state-of-the-art performance.