在评论情感分析的研究中,和评论相关的用户与产品信息对于提高情感分类的准确率有很大的帮助。为了能够有效地利用产品和用户信息,并构建产品和用户信息与评论之间的关联,该文提出一种基于图网络的模型,将产品与用户信息和评论之间的关系构建为一个图,并基于图卷积网络模型学习产品与用户信息对评论的影响,从而提升评论情感分类的准确率。在Yelp2013数据集上进行实验,实验结果表明,该模型能有效地提高评论的情感分类准确率。
Abstract
In review sentiment analysis, the user and product information related to the review are of great help to improve the accuracy. This paper proposes a graph network-based model to capture the relationship between products and user information and reviews via a graph. It learns the impact of product and user information on reviews based on the graph convolutional network model. Experiments on the Yelp2013 dataset show that the model can effectively improve the accuracy of emotion classification for user comments.
关键词
图卷积 /
神经网络 /
情感分类
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Key words
graph convolution /
neural network /
sentiment classification
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参考文献
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脚注
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基金
国家自然科学基金青年科学基金(61806137,61702518);江苏省高等学校自然科学研究面上项目(18KJB520043)
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