计算机科学
情绪分析
编码
判决
解析
人工智能
依赖关系(UML)
依存语法
图形
自然语言处理
语法
树(集合论)
树形结构
学期
机器学习
理论计算机科学
数据结构
任务(项目管理)
数学分析
程序设计语言
管理
化学
经济
基因
生物化学
数学
作者
Kai Wang,Weizhou Shen,Yunyi Yang,Xiaojun Quan,Rui Wang
出处
期刊:Cornell University - arXiv
日期:2020-04-26
被引量:6
标识
DOI:10.48550/arxiv.2004.12362
摘要
Aspect-based sentiment analysis aims to determine the sentiment polarity towards a specific aspect in online reviews. Most recent efforts adopt attention-based neural network models to implicitly connect aspects with opinion words. However, due to the complexity of language and the existence of multiple aspects in a single sentence, these models often confuse the connections. In this paper, we address this problem by means of effective encoding of syntax information. Firstly, we define a unified aspect-oriented dependency tree structure rooted at a target aspect by reshaping and pruning an ordinary dependency parse tree. Then, we propose a relational graph attention network (R-GAT) to encode the new tree structure for sentiment prediction. Extensive experiments are conducted on the SemEval 2014 and Twitter datasets, and the experimental results confirm that the connections between aspects and opinion words can be better established with our approach, and the performance of the graph attention network (GAT) is significantly improved as a consequence.
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