Abstract：In the aspect-level sentiment classification task, the abilities of the existing methods to enhance aspect terms information and utilize local feature information are weak. To settle this problem, a feature fusion learning network(FFLN) is proposed. Firstly, comments are processed into text, aspect and text-aspect as input. After obtaining vector representation of the input by bidirectional encoder representation from the transformers model, the attention encoder is utilized to obtain the hidden state of the context and aspect items and extract the semantic information. Then, based on the hidden state feature, aspect-specific text vector representation is generated using aspect-specific transformation component to integrate aspect terms information into context representation. Finally, the local features are extracted from aspect-specific text vector by the context position weighted module. The final representation features are obtained by the fusion learning of global and local features, and sentiment classification is conducted. Experiments on classical English datasets and Chinese review datasets show that FFLN improves the classification effect.
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2021, Vol. 34 
