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Aspect-Level Sentiment Analysis Based on Deep Learning

Computers, Materials & Continua 2024年第3期78卷 3743-3762页

作者： Mengqi Zhang Jiazhao Chai Jianxiang Cao Jialing Ji Tong Yi Department of Communication University of New South WalesSydney2052Australia Higher Vocational Education Publishing Division Higher Education PressBeijing100029China State Key Laboratory of Media Convergence and Communication Communication University of ChinaBeijing100020China Key Lab of Education Blockchain and Intelligent Technology Ministry of EducationGuangxi Normal UniversityGuilin541004China

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.

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

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Neural Attentional Relation Extraction with Dual dependency trees

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Journal of Computer Science & Technology 2022年第6期37卷 1369-1381页

作者：李冬雷智磊宋宝燕纪婉婷寇月 School of Information Liaoning UniversityShenyang 110036China School of Computer Science and Engineering Northeastern UniversityShenyang 110004China

Relation extraction has been widely used to find semantic relations between entities from plain *** trees provide deeper semantic information for relation ***,existing dependency tree based models adopt pruning strategies that are too aggressive or conservative,leading to insufficient semantic information or excessive noise in relation extraction *** overcome this issue,we propose the Neural Attentional Relation Extraction Model with Dual dependency trees(called DDT-REM),which takes advantage of both the syntactic dependency tree and the semantic dependency tree to well capture syntactic features and semantic features,***,we first propose novel representation learning to capture the dependency relations from both syntax and ***,for the syntactic dependency tree,we propose a local-global attention mechanism to solve semantic *** design an extension of graph convolutional networks(GCNs)to perform relation extraction,which effectively improves the extraction *** conduct experimental studies based on three real-world *** with the traditional methods,our method improves the F 1 scores by 0.3,0.1 and 1.6 on three real-world datasets,respectively.

关键词： relation extraction graph convolutional network(GCN) syntactic dependency tree semantic dependency tree

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