Abstract：To solve the problems of weak perception for aspect words and generalization ability in the existing models for sentiment classification, a context-oriented attention joint learning network for aspect-level sentiment classification(CAJLN) is proposed. Firstly, the bidirectional encoder representation from transformers(BERT) model is employed as the encoder to preprocess short texts into sentences, sentence pairs and aspect words as input, and their hidden features are extracted through the single sentence and sentence pair classification models, respectively. Then, based on the hidden features of sentences and aspect words, attention mechanisms for sentences and aspect words are established to obtain aspect-specific context-aware representation. Then, the hidden features of sentence pairs and aspect-specific context-aware representations are learned jointly. Xavier normal distribution is utilized to initialize the weights. Thus, the continuous updating of the parameters during the back propagation is ensured, and useful information is learned by CAJLN in the training process. Experiments show that CAJLN effectively improves the performance of sentiment classification for short text on multiple datasets.
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2020, Vol. 33 
