重庆邮电大学学报（自然科学版）2023，Vol.35Issue(6)：1164-1173,10.DOI:10.3979/j.issn.1673-825X.202209070234
基于关系图卷积神经网络与双注意力的方面级情感分析
Aspect-level sentiment analysis based on relational graph convolutional neural network and bidirectional attention
摘要
Abstract
Most existing aspect-level sentiment analysis models based on syntactic dependency tree only focus on the syn-tactic dependency structure of sentences,ignoring the positional semantic relationship between words.At the same time,ex-isting models only focus on the output of the last layer of the graph convolutional neural network and cannot learn from dif-ferent graph convolutional layers.To address this problem,this paper proposes an aspect-level sentiment analysis model based on relational graph convolutional neural network and bidirectional attention.First,we extract the positional semantic relationship of the text through relationship-aware attention,and combine it with syntax dependency tree to obtain rich structural information in the text.Then we use graph convolutional neural network to extract the deep representation of as-pect words.Finally,we use the bidirectional attention mechanism to fuse the output of different graph convolutional layers and combine the deep representation of aspect words and context information for emotional classification.Experimental re-sults on semval14 and twitter datasets show that the graph convolutional network and the bidirectional attention structure can effectively improve the overall performance of the model compared with the benchmark experiment.关键词
方面级情感分析/关系感知注意力/双注意力/图卷积神经网络Key words
aspect-level sentiment analysis/relationship-aware attention/bidirectional attention/graph convolutional neural network分类
信息技术与安全科学引用本文复制引用
方云龙,李卫疆..基于关系图卷积神经网络与双注意力的方面级情感分析[J].重庆邮电大学学报（自然科学版）,2023,35(6):1164-1173,10.基金项目
国家自然科学基金项目(62066022)The National Natural Science Foundation of China(62066022) （62066022）
