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Article
Computer Science, Artificial Intelligence
Bin Liang et al.
Summary: This paper proposes a graph convolutional network model Sentic GCN based on SenticNet to enhance the affective dependencies of sentences for aspect-based sentiment analysis. By integrating emotional knowledge from SenticNet, the model effectively handles contextual affective information in sentences, improving the effectiveness of sentiment polarity detection towards specific aspects.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Bo Huang et al.
Summary: In recent years, researchers have paid more attention to aspect-level sentiment analysis in natural language processing. A fine-grained sentiment analysis distinguishes each aspect of the text and makes separate judgments on the sentiment polarity. This paper proposes an aspect-level sentiment analysis model with aspect-specific contextual location information, adjusting the weight of contextual words and extracting the influence of contextual association on individual sentence sentiment polarity.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Haiyan Wu et al.
Summary: Aspect-based Sentiment Analysis (ABSA) is a method that aims to identify the sentiment polarity of specific aspects in a sentence. This paper proposes a phrase dependency graph attention network (PD-RGAT) based on a relational graph constructed from the phrase dependency graph, and experimental results demonstrate its effectiveness.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Computer Science, Information Systems
Jian Liao et al.
Summary: This paper proposes a method for learning the implication of implicit sentiment using sentimental commonsense knowledge graph embedding and multi-polarity orthogonal attention. By automatically extracting useful knowledge tuples through a matching and filtering method, the sentimental knowledge is combined with semantic embedding, improving the performance of implicit sentiment analysis.
INFORMATION PROCESSING & MANAGEMENT
(2022)
Article
Computer Science, Information Systems
Jiandian Zeng et al.
Summary: Aspect-level sentiment classification aims to classify fine-grained sentiment polarities of different aspects in a sentence. Existing methods overlook the aspect relations, while this paper proposes a new multi-task learning network that extracts aspect relations to improve the classification results, and conducts experiments on multiple datasets to demonstrate its effectiveness.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Information Systems
Huyen Trang Phan et al.
Summary: This study proposes a new method called the CANN-SSCG model to address the limitations of graph convolutional network (GCN)-based aspect-level sentiment analysis methods. The method constructs three different heterogeneous graphs and combines them into a general heterogeneous graph. Finally, a convolutional neural network algorithm is used for aspect-level sentiment analysis, and promising results are achieved.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Artificial Intelligence
Hu Huang et al.
Summary: Aspect-based sentiment analysis helps service providers understand users' opinions in online posts, and the proposed logic tensor network with massive rules significantly improves the accuracy of the analysis.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Computer Science, Information Systems
Meng Zhao et al.
Summary: This paper proposes an aggregated graph convolutional network (AGCN) to enhance the representation ability of target nodes in aspect-based sentiment analysis. The AGCN updates the node representation iteratively using aggregator functions, and uses subdependency and attention mechanism to extract and capture sentiment dependencies between node feature information. Experimental results show that AGCN is effective compared to other GCN-based methods.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Information Systems
Baiyu Yang et al.
Summary: This paper introduces a model for sentiment classification task on specific aspects, achieving superior performance in capturing the correspondences between aspects and opinion words in a sentence.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Artificial Intelligence
Lan You et al.
Summary: This paper introduces a sentiment knowledge-adaptive pretraining model (ASK-RoBERTa) that predicts sentiment polarities of different aspects by building a sentiment word dictionary and optimizing mining rules. The experimental results on multiple public benchmark datasets demonstrate the satisfactory performance of ASK-RoBERTa.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Xiaofei Zhu et al.
Summary: The study introduces a Global and Local Dependency Guided Graph Convolutional Networks (GL-GCN) approach, which integrates global and local structure information into aspect-based sentiment classification. Experimental results demonstrate that this method outperforms existing approaches in terms of accuracy and F1-Score.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Acoustics
Xuefeng Bai et al.
Summary: Researchers propose a novel approach, a relational graph attention network that integrates typed syntactic dependency information, to improve targeted sentiment classification performance, which achieves good results. This method effectively utilizes label information and outperforms existing state-of-the-art syntax-based approaches.
IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING
(2021)
Article
Computer Science, Artificial Intelligence
Huy-Thanh Nguyen et al.
EXPERT SYSTEMS WITH APPLICATIONS
(2020)
Article
Computer Science, Artificial Intelligence
Jiyao Wei et al.
Article
Computer Science, Artificial Intelligence
Jie Zhou et al.
KNOWLEDGE-BASED SYSTEMS
(2020)
Article
Acoustics
Bowen Zhang et al.
IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING
(2020)
