基于双通道语义差网络的方面级别情感分类

曾碧卿,徐马一,杨健豪,裴枫华,甘子邦,丁美荣,程良伦

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中文信息学报 ›› 2022, Vol. 36 ›› Issue (12) : 159-172.
情感分析与社会计算

基于双通道语义差网络的方面级别情感分类

  • 曾碧卿1,徐马一1,杨健豪1,裴枫华1,甘子邦1,丁美荣1,程良伦2
作者信息 +

Aspect-level Sentiment Classification Based on Double Channel Semantic Difference Network

  • ZENG Biqing1, XU Mayi1, YANG Jianhao1, PEI Fenghua 1, GAN Zibang1,
    DING Meirong 1, CHENG Lianglun2
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摘要

方面级别情感分类旨在分析一个句子中不同方面词的情感极性。先前的研究在文本表示上,难以产生依赖于特定方面词的上下文表示;在语义特征分析上,忽略了方面词的双侧文本在整体语义上与方面词情感极性之间具备不同关联度这一特征。针对上述问题,该文设计了一种双通道交互架构,同时提出了语义差这一概念,并据此构建了双通道语义差网络。双通道语义差网络利用双通道架构捕捉相同文本中不同方面词的上下文特征信息,并通过语义提取网络对双通道中的文本进行语义特征提取,最后利用语义差注意力增强模型对重点信息的关注。该文在SemEval2014的Laptop和Restaurant数据集以及ACL的Twitter数据集上进行了实验,分类准确率分别达到了81.35%、86.34%和78.18%,整体性能超过了所对比的基线模型。

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.

关键词

自然语言处理 / 方面级别情感分析 / 双通道架构 / 语义差注意力

Key words

natural language processing / aspect-level sentiment classification / double channel architecture / semantic difference attention

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曾碧卿,徐马一,杨健豪,裴枫华,甘子邦,丁美荣,程良伦. 基于双通道语义差网络的方面级别情感分类. 中文信息学报. 2022, 36(12): 159-172
ZENG Biqing, XU Mayi, YANG Jianhao, PEI Fenghua , GAN Zibang,
DING Meirong , CHENG Lianglun. Aspect-level Sentiment Classification Based on Double Channel Semantic Difference Network. Journal of Chinese Information Processing. 2022, 36(12): 159-172

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基金

国家自然科学基金(61876067);广东省普通高校人工智能重点领域专项(2019KZDZX1033);广东省信息物理融合系统重点实验室建设专项(2020B1212060069)
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