{"title":"CABiLSTM-BERT: Aspect-based sentiment analysis model based on deep implicit feature extraction","authors":"Bo He, Ruoyu Zhao, Dali Tang","doi":"10.1016/j.knosys.2024.112782","DOIUrl":null,"url":null,"abstract":"<div><div>Aspect-based sentiment analysis (ABSA) models typically focus on learning contextual syntactic information and dependency relations. However, these models often struggle with losing or forgetting implicit feature information from shallow and intermediate layers during the learning process, potentially compromising classification performance. We consider the implicit feature information in each layer of the model to be equally important for processing. So, this paper proposes the CABiLSTM-BERT model, which aims to fully leverage implicit features at each layer to address this information loss problem and improve accuracy. The CABiLSTM-BERT model employs a frozen BERT pre-trained model to extract text word vector features, reducing overfitting and accelerating training. These word vectors are then processed through CABiLSTM, which preserves implicit feature representations of input sequences and LSTMs in each direction and layer. The model applies convolution to merge all features into a set of embedding representations after highlighting important features through multi-head self-attention calculations for each feature group. This approach minimizes information loss and maximizes utilization of important implicit feature information at each layer. Finally, the feature representations undergo average pooling before passing through the sentiment classification layer for polarity prediction. The effectiveness of the CABiLSTM-BERT model is validated using five publicly available real-world datasets and evaluated using metrics such as accuracy and Macro-F1. Results demonstrate the model's efficacy in addressing ABSA tasks.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"309 ","pages":"Article 112782"},"PeriodicalIF":7.2000,"publicationDate":"2024-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Knowledge-Based Systems","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0950705124014163","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
Abstract
Aspect-based sentiment analysis (ABSA) models typically focus on learning contextual syntactic information and dependency relations. However, these models often struggle with losing or forgetting implicit feature information from shallow and intermediate layers during the learning process, potentially compromising classification performance. We consider the implicit feature information in each layer of the model to be equally important for processing. So, this paper proposes the CABiLSTM-BERT model, which aims to fully leverage implicit features at each layer to address this information loss problem and improve accuracy. The CABiLSTM-BERT model employs a frozen BERT pre-trained model to extract text word vector features, reducing overfitting and accelerating training. These word vectors are then processed through CABiLSTM, which preserves implicit feature representations of input sequences and LSTMs in each direction and layer. The model applies convolution to merge all features into a set of embedding representations after highlighting important features through multi-head self-attention calculations for each feature group. This approach minimizes information loss and maximizes utilization of important implicit feature information at each layer. Finally, the feature representations undergo average pooling before passing through the sentiment classification layer for polarity prediction. The effectiveness of the CABiLSTM-BERT model is validated using five publicly available real-world datasets and evaluated using metrics such as accuracy and Macro-F1. Results demonstrate the model's efficacy in addressing ABSA tasks.
期刊介绍:
Knowledge-Based Systems, an international and interdisciplinary journal in artificial intelligence, publishes original, innovative, and creative research results in the field. It focuses on knowledge-based and other artificial intelligence techniques-based systems. The journal aims to support human prediction and decision-making through data science and computation techniques, provide a balanced coverage of theory and practical study, and encourage the development and implementation of knowledge-based intelligence models, methods, systems, and software tools. Applications in business, government, education, engineering, and healthcare are emphasized.