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Cross-domain sentiment classification based on key pivot and non-pivot extraction

机译：基于关键枢轴和非枢轴提取的跨域情绪分类

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摘要

Cross-domain sentiment classification (CDSC) aims to predict the sentiment polarity of reviews in an unsupervised target domain by utilizing a classifier learned from a supervised source domain. Most existing methods mainly construct a knowledge transfer model to enhance the category consistency between the source and target domains. These methods not only have little interpretation of transferable information, but are also weak at capturing the sentiment characteristics of a specific domain. For cross-domain reviews, pivots, that is, the domain-shared sentiment words and non pivots, that is, the domain-specific sentiment words, are critical sentiment clues in the CDSC task. In this study, we comprehensively use domain-shared and domain-specific sentiment information to construct a knowledge transfer model with interpretability for the CDSC task. Specifically, we construct a novel hierarchical attention network called KPE-net, which automatically extracts pivots between two domains. When pivots are used as the bridge, a joint attention learning network called NKPE-net is built to capture non-pivots of different domains. Finally, combining the sentiment factors generated by key pivots and non-pivots, a sentiment-sensitive network model (SSNM) is proposed to realize the transfer of attention to emotions across domains. Experiments on the Amazon review dataset demonstrated the superiority of the proposed model. (C) 2021 Elsevier B.V. All rights reserved.
机译：跨域情绪分类（CDSC）旨在通过利用来自监督源域中学习的分类器来预测无监督目标域中评测的情感极性。大多数现有方法主要构建知识传输模型，以增强源和目标域之间的类别一致性。这些方法不仅具有几乎没有对可转移信息的解释，而且在捕获特定领域的情绪特征时也是薄弱的。对于跨域评论，竞争，即域共享情绪单词和非竞争，即特定于域的情绪字，是CDSC任务中的关键情绪线索。在本研究中，我们全面地使用域共享和域特定的情绪信息来构建具有CDSC任务的解释性的知识传输模型。具体而言，我们构建一个名为KPE-Net的新型分层关注网络，其自动提取两个域之间的枢轴。当枢轴用作桥时，建立了一个名为NKPE-Net的联合注意力学习网络，以捕获不同域的非枢轴。最后，组合键枢轴产生的情绪因素和非枢轴，提出了一种情感敏感网络模型（SSNM）来实现关注域跨域的情绪。亚马逊评论数据集的实验表明了所提出的模型的优越性。 （c）2021 elestvier b.v.保留所有权利。

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  • 来源
    《Knowledge-Based Systems》 |2021年第27期|107280.1-107280.14|共14页
  • 作者

    Fu Yanping; Liu Yun;

  • 作者单位

    Beijing Jiaotong Univ Sch Elect & Informat Engn Key Lab Commun & Informat Syst Beijing Municipal Commiss Educ Beijing 100044 Peoples R China;

    Beijing Jiaotong Univ Sch Elect & Informat Engn Key Lab Commun & Informat Syst Beijing Municipal Commiss Educ Beijing 100044 Peoples R China;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Cross-domain; Pivot; Non-pivot; Bi-GRU; Sentiment classification;

    机译：跨域;枢轴;非枢轴;Bi-Gru;感情分类;

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