基于双通道语义差网络的方面级别情感分类

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Abstract Aspect-Level sentiment classification aims to analyze the sentiment polarity of different aspect words in a sentence. To realize aspect-word aware contextual representations, this paper proposes a double channel semantic difference network(DCSDN) with the notation of theory of Semantic Difference. The DCSDN captures the contextual feature information of different aspects in the same text with the double channel architecture, and extract the semantic features of the texts in the double channel via a semantic extraction network. It employs the semantic difference attention to enhance the attention to key information. Experiments on Laptop datasets and Restaurant datasets (SemEval2014) and the Twitter dataset(ACL) demonstrate the accuracy reaching 81.35%, 86.34% and 78.18% respectively.

Key words： natural language processing aspect-level sentiment classification double channel architecture semantic difference attention

Received: 08 December 2020

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	Articles by authors
	ZENG Biqing
	XU Mayi
	YANG Jianhao
	PEI Fenghua
	GAN Zibang
	DING Meirong
	CHENG Lianglun

Cite this article:

ZENG Biqing,XU Mayi,YANG Jianhao, et al. Aspect-level Sentiment Classification Based on Double Channel Semantic Difference Network[J]. , 2022, 36(12): 159-172.

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http://jcip.cipsc.org.cn/EN/ OR http://jcip.cipsc.org.cn/EN/Y2022/V36/I12/159

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