Paper Info
Title
Aspect-based sentiment analysis via affective knowledge enhanced graph convolutional networks
Abstract
Aspect-based sentiment analysis is a fine-grained sentiment analysis task, which needs to detection the sentiment polarity towards a given aspect. Recently, graph neural models over the dependency tree are widely applied for aspect-based sentiment analysis. Most existing works, however, they generally focus on learning the dependency information from contextual words to aspect words based on the dependency tree of the sentence, which lacks the exploitation of contextual affective knowledge with regard to the specific aspect. In this paper, we propose a graph convolutional network based on SenticNet to leverage the affective dependencies of the sentence according to the specific aspect, called Sentic GCN. To be specific, we explore a novel solution to construct the graph neural networks via integrating the affective knowledge from SenticNet to enhance the dependency graphs of sentences. Based on it, both the dependencies of contextual words and aspect words and the affective information between opinion words and the aspect are considered by the novel affective enhanced graph model. Experimental results on multiple public benchmark datasets illustrate that our proposed model can beat state-of-the-art methods.
Year
DOI
Venue
2022
10.1016/j.knosys.2021.107643
Knowledge-Based Systems
Keywords
DocType
Volume
Sentiment analysis,Aspect sentiment analysis,Affective knowledge,Graph convolutional networks
Journal
235
ISSN
Citations 
PageRank 
0950-7051
6
0.47
References 
Authors
11
5
Name
Order
Citations
PageRank
Liang Bin123954.58
Hang Su244847.57
Lin Gui39412.82
Erik Cambria4102.23
Xu Ruifeng543253.04