Abstract: Aiming at the problem of information loss in encoding long-distance texts as using recurrent neural networks, and the problem of attention bias to the high-frequency sentiment information when using the attention mechanism to extract sentiment information, this paper proposes a method of aspect-level sentiment classification based on multi-pattern feature fusion. The method divides sentiment information into three categories:single-point sentiment information, multi-point sentiment information and partial sentiment information with different expression patterns, and realizes mutual confirmation and error correction among various types of features by focusing on, extracting and fusing the three types of emotion information. The problems of information loss and attention bias are reduced, and the ability of aspect-level sentiment classification under complex sentiment expression patterns is enhanced. Experimental results show that the accuracy and F1 value of the aspect-level sentiment classification task in extracting and fusing sentiment information can be significantly improved by using the proposed method.

Key words: sentiment classification, multi-pattern fusion, aspect information, attention mechanism, recurrent neural network (RNN)