.png){kind=icon}
返回
Sentiment classification via user and product interactive modeling
.png){kind=icon}
PRE
AI
DOI：10.1007/s11432-020-3116-x.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Sentiment classification aims to identify the polarity of a given review. Most existing methods consider each review as an individual while ignoring the importance of the user and product information of the given review. A direct way to integrate user and product information is to employ an attention mechanism to learn the local interaction between them. However, local interactions cannot capture the global optimization among user and product information. Therefore, we propose a novel interactive model to integrate both local and global interactions between users and products. In particular, we employ an attention mechanism to learn local interactions between users and products, and construct user and product interactive graphs to model the global interaction of users and products. Empirical evaluation shows that our model outperforms previous state-of-the-art methods significantly by learning the local and global interactions among users' preferences, product characteristics, and reviews.
Keyword:
product review analysis
sentiment classification
interactive graph
graph convolutional network
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
7.6
论文数:
4.9K
被引数:
8.9K
机构
引用论文
.png){kind=icon}
Performance evaluation of solar distillation using vacuum tube coupled with photovoltaic system
PREAI
分享收藏
查看更多内容