{"title":"基于图卷积网络和交互聚合注意力的方面级情感分析","authors":"Yuxin Wu, Guofeng Deng","doi":"10.1016/j.csl.2025.101819","DOIUrl":null,"url":null,"abstract":"<div><div>Sentiment analysis has always been an important task in the artificial intelligence field, and aspect-level sentiment analysis involves fine-grained sentiment analysis. Recently, graph convolutional networks (GCNs) built on sentence dependency trees have been widely used in aspect-level sentiment analysis. Because GCNs have good aggregation effects, they can efficiently aggregate the information of neighboring nodes. However, many previous studies concerning graph neural networks only focused on the information between nodes and did not effectively explore the connections between aspects and sentences or highlight the parts with high aspect relevance. To address this problem, we propose a new GCN. When constructing a dependency tree-based graph, affective information and position index information are added to each node to enhance the graph. In addition, we use an interactive aggregate attention mechanism, which utilizes the aggregated information related to the connections between aspects and sentences from the GCN to highlight the important parts so that the model can fully learn the relationships between aspects and sentences. Finally, we validate our model on four public benchmark datasets and attain improvements over the state-of-the-art methods.</div></div>","PeriodicalId":50638,"journal":{"name":"Computer Speech and Language","volume":"95 ","pages":"Article 101819"},"PeriodicalIF":3.4000,"publicationDate":"2025-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Aspect-level sentiment analysis based on graph convolutional networks and interactive aggregate attention\",\"authors\":\"Yuxin Wu, Guofeng Deng\",\"doi\":\"10.1016/j.csl.2025.101819\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Sentiment analysis has always been an important task in the artificial intelligence field, and aspect-level sentiment analysis involves fine-grained sentiment analysis. Recently, graph convolutional networks (GCNs) built on sentence dependency trees have been widely used in aspect-level sentiment analysis. Because GCNs have good aggregation effects, they can efficiently aggregate the information of neighboring nodes. However, many previous studies concerning graph neural networks only focused on the information between nodes and did not effectively explore the connections between aspects and sentences or highlight the parts with high aspect relevance. To address this problem, we propose a new GCN. When constructing a dependency tree-based graph, affective information and position index information are added to each node to enhance the graph. In addition, we use an interactive aggregate attention mechanism, which utilizes the aggregated information related to the connections between aspects and sentences from the GCN to highlight the important parts so that the model can fully learn the relationships between aspects and sentences. Finally, we validate our model on four public benchmark datasets and attain improvements over the state-of-the-art methods.</div></div>\",\"PeriodicalId\":50638,\"journal\":{\"name\":\"Computer Speech and Language\",\"volume\":\"95 \",\"pages\":\"Article 101819\"},\"PeriodicalIF\":3.4000,\"publicationDate\":\"2025-05-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Computer Speech and Language\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0885230825000440\",\"RegionNum\":3,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computer Speech and Language","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0885230825000440","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
Aspect-level sentiment analysis based on graph convolutional networks and interactive aggregate attention
Sentiment analysis has always been an important task in the artificial intelligence field, and aspect-level sentiment analysis involves fine-grained sentiment analysis. Recently, graph convolutional networks (GCNs) built on sentence dependency trees have been widely used in aspect-level sentiment analysis. Because GCNs have good aggregation effects, they can efficiently aggregate the information of neighboring nodes. However, many previous studies concerning graph neural networks only focused on the information between nodes and did not effectively explore the connections between aspects and sentences or highlight the parts with high aspect relevance. To address this problem, we propose a new GCN. When constructing a dependency tree-based graph, affective information and position index information are added to each node to enhance the graph. In addition, we use an interactive aggregate attention mechanism, which utilizes the aggregated information related to the connections between aspects and sentences from the GCN to highlight the important parts so that the model can fully learn the relationships between aspects and sentences. Finally, we validate our model on four public benchmark datasets and attain improvements over the state-of-the-art methods.
期刊介绍:
Computer Speech & Language publishes reports of original research related to the recognition, understanding, production, coding and mining of speech and language.
The speech and language sciences have a long history, but it is only relatively recently that large-scale implementation of and experimentation with complex models of speech and language processing has become feasible. Such research is often carried out somewhat separately by practitioners of artificial intelligence, computer science, electronic engineering, information retrieval, linguistics, phonetics, or psychology.