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J. Patt. Recogn. Artif. Intell."],"published-print":{"date-parts":[[2022,3,15]]},"abstract":"<jats:p> Aspect-level sentiment analysis aims to predict the sentiment polarity toward a specific aspect in a sentence. Most current approaches are based on deep learning and the attention mechanism. However, these models cannot simultaneously include the context semantic information carried by local words and the global syntactic information possibly carried by remote words. In this paper, we propose a local semantic and global syntactic integration scheme, which employs a local focus mechanism over local context words and exploits improved graph convolutional networks over dependency tree to encode global syntactic information. Moreover, multi-head attention is used to capture both the semantic information and also the interactive information between semantics and syntactic features. 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