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Cross-domain sentiment analysis method based on GCN under lifelong learning framework

A sentiment analysis, cross-domain technology, applied in the cross-domain sentiment analysis field based on GCN under the framework of lifelong learning, which can solve the problems of no translation invariance and different graph structures.

Active Publication Date: 2021-01-05
NORTHEASTERN UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] Problems: Traditional supervised learning algorithms have been proven to be effective in dealing with sentiment classification problems, and are widely used in the task of predicting the sentiment polarity of reviews given a domain
But the graph structure is different from the matrix structure, and the situation near each node may be different from each other, so it does not have translation invariance

Method used

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  • Cross-domain sentiment analysis method based on GCN under lifelong learning framework
  • Cross-domain sentiment analysis method based on GCN under lifelong learning framework
  • Cross-domain sentiment analysis method based on GCN under lifelong learning framework

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Embodiment Construction

[0043] In order to make the technical means, creative features, goals and effects achieved by the present invention easy to understand, the present invention will be further elaborated below in conjunction with specific embodiments and accompanying drawings, but the following embodiments are only preferred embodiments of the present invention, not all . Based on the examples in the implementation manners, other examples obtained by those skilled in the art without making creative efforts all belong to the protection scope of the present invention.

[0044] Specific embodiments of the present invention will be described below in conjunction with the accompanying drawings.

[0045] Dataset 1

[0046] In the design of the experimental CDS-GCN algorithm, we selected the cross-domain sentiment classification dataset written by Blitzer et al. as the experimental dataset. In this paper, the original part of the cross-domain sentiment classification dataset without data preprocessing...

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Abstract

The invention provides a cross-domain sentiment analysis method based on GCN under a lifelong learning framework, relates to the field of cross-domain sentiment classification in natural language processing, and is realized by the following process: proposing a cross-domain sentiment classification algorithm based on a graph convolutional neural network, namely CDSGCN, on the basis of proposing the CDSGCN, proposing a cross-domain sentiment classification algorithm based on a graph convolutional neural network under a lifelong learning framework, namely LLCDSGCN in combination with a lifelonglearning thought, wherein the lifelong learning is different from transfer learning or multi-task learning and other related learning tasks due to the characteristics, the limitation of isolated learning is broken through, the influence caused by time-consuming and labor-consuming manual data annotation is relieved, and the characteristics are in accordance with the initial intention of cross-domain emotion classification tasks.

Description

technical field [0001] The invention relates to the field of cross-domain sentiment classification in natural language processing, in particular to a GCN-based cross-domain sentiment analysis method under a lifelong learning framework. Background technique [0002] The popularization of the Internet has brought a larger user group to merchants, and also brought more user feedback information. Users can easily express their views and opinions on the products they use or receive services on the Internet, such as a large number of product reviews. Can be easily obtained on Douban (movie reviews), Amazon (book reviews), Meituan (restaurant reviews), etc. Document-level sentiment classification refers to classifying a given review according to the sentiment expressed by the review author. The task is very important. First of all, for merchants, consumers’ evaluations of products reflect their likes and dislikes for product quality or services. Secondly, consumers who have not pur...

Claims

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Application Information

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IPC IPC(8): G06F16/35G06F16/33G06K9/62G06N3/04G06N3/08
CPCG06F16/35G06F16/3346G06N3/08G06N3/045G06F18/2415
Inventor 韩东红白霖向伟豪王波涛吴刚乔百友
Owner NORTHEASTERN UNIV
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