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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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aspect-level sentiment analysis Incorporating Semantic and Syntactic Information

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Journal of Computer and Communications 2024年第1期12卷 191-207页

作者： Jiachen Yang Yegang Li Hao Zhang Junpeng Hu Rujiang Bai School of Computer Science and Technology Shandong University of Technology Zibo China Institute of Information Management Shandong University of Technology Zibo China

Aiming at the problem that existing models in aspect-level sentiment analysis cannot fully and effectively utilize sentence semantic and syntactic structure information, this paper proposes a graph neural network-based aspect-level sentiment classification model. Self-attention, aspectual word multi-head attention and dependent syntactic relations are fused and the node representations are enhanced with graph convolutional networks to enable the model to fully learn the global semantic and syntactic structural information of sentences. Experimental results show that the model performs well on three public benchmark datasets Rest14, Lap14, and Twitter, improving the accuracy of sentiment classification.

关键词： aspect-level sentiment analysis Attentional Mechanisms Dependent Syntactic Trees Graph Convolutional Neural Networks

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