Aspect-Level Sentiment Analysis Based on Deep Learning

作     者：Mengqi Zhang Jiazhao Chai Jianxiang Cao Jialing Ji Tong Yi 

作者机构：Department of CommunicationUniversity of New South WalesSydney2052Australia Higher Vocational Education Publishing DivisionHigher Education PressBeijing100029China State Key Laboratory of Media Convergence and CommunicationCommunication University of ChinaBeijing100020China Key Lab of Education Blockchain and Intelligent TechnologyMinistry of EducationGuangxi Normal UniversityGuilin541004China 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2024年第78卷第3期

页      面：3743-3762页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0805[工学-材料科学与工程（可授工学、理学学位）] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：This work is partly supported by the Fundamental Research Funds for the Central Universities(CUC230A013) It is partly supported by Natural Science Foundation of Beijing Municipality(No.4222038) It is also supported by National Natural Science Foundation of China(Grant No.62176240) 

主　　题：Aspect-level sentiment analysis deep learning graph convolutional neural network user features syntactic dependency tree 

摘      要：In recent years,deep learning methods have developed rapidly and found application in many fields,including natural language *** the field of aspect-level sentiment analysis,deep learning methods can also greatly improve the performance of ***,previous studies did not take into account the relationship between user feature extraction and contextual *** address this issue,we use data feature extraction and deep learning combined to develop an aspect-level sentiment analysis *** be specific,we design user comment feature extraction(UCFE)to distill salient features from users’historical comments and transform them into representative user feature ***,the aspect-sentence graph convolutional neural network(ASGCN)is used to incorporate innovative techniques for calculating adjacency matrices;meanwhile,ASGCN emphasizes capturing nuanced semantics within relationships among aspect words and syntactic dependency ***,three embedding methods are devised to embed the user feature vector into the ASGCN *** empirical validations verify the effectiveness of these models,consistently surpassing conventional benchmarks and reaffirming the indispensable role of deep learning in advancing sentiment analysis methodologies.