Open AccessImplicit sentiment analysis based on graph attention neural network
Author(s) -
Yang Shanliang,
Xing Linlin,
Li Yongming,
Chang Zheng
Publication year - 2022
Publication title -
engineering reports
Language(s) - English
Resource type - Journals
ISSN - 2577-8196
DOI - 10.1002/eng2.12452
Subject(s) - computer science , sentiment analysis , constraint (computer aided design) , sentence , convolutional neural network , graph , artificial intelligence , benchmark (surveying) , expression (computer science) , natural language processing , artificial neural network , theoretical computer science , mathematics , geometry , geodesy , programming language , geography
Sentiment analysis is one of the crucial tasks in the field of natural language processing. Implicit sentiment suffers a significant challenge because the sentence does not include explicit emotional words and emotional expression is vague. This paper proposed a novel implicit sentiment analysis model based on graph attention convolutional neural network. A graph convolutional neural network is used to propagate semantic information. The attention mechanism is employed to compute the contribution to the emotional expression of words. In order to solve the problem of multiple attention preserving repeated information, orthogonal attention constraint was used to make different attention store different emotional information; given the uneven distribution of emotional information, score attention constraint was proposed to make the model focus on a limited number of essential words. The performance of the proposed model was verified on implicit sentiment datasets. The F value reached 88.16%, which is higher than the benchmark model in the literature. The attention mechanism is analyzed to verify the effectiveness of orthogonal constraint and score constraint.