Next Article in Journal
Sort and Deep-SORT Based Multi-Object Tracking for Mobile Robotics: Evaluation with New Data Association Metrics
Next Article in Special Issue
Sentence Boundary Extraction from Scientific Literature of Electric Double Layer Capacitor Domain: Tools and Techniques
Previous Article in Journal
Path Planning Based on NURBS for Hyper-Redundant Manipulator Used in Narrow Space
Previous Article in Special Issue
Latent-Cause Extraction Model in Maritime Collision Accidents Using Text Analytics on Korean Maritime Accident Verdicts
 
 
Article

Transformer-Based Graph Convolutional Network for Sentiment Analysis

School of Computer Science and Engineering, Central South University, Changsha 410083, China
*
Author to whom correspondence should be addressed.
Academic Editor: Evgeny Nikulchev
Appl. Sci. 2022, 12(3), 1316; https://doi.org/10.3390/app12031316
Received: 21 November 2021 / Revised: 19 January 2022 / Accepted: 20 January 2022 / Published: 26 January 2022
(This article belongs to the Special Issue Natural Language Processing: Approaches and Applications)
Sentiment Analysis is an essential research topic in the field of natural language processing (NLP) and has attracted the attention of many researchers in the last few years. Recently, deep neural network (DNN) models have been used for sentiment analysis tasks, achieving promising results. Although these models can analyze sequences of arbitrary length, utilizing them in the feature extraction layer of a DNN increases the dimensionality of the feature space. More recently, graph neural networks (GNNs) have achieved a promising performance in different NLP tasks. However, previous models cannot be transferred to a large corpus and neglect the heterogeneity of textual graphs. To overcome these difficulties, we propose a new Transformer-based graph convolutional network for heterogeneous graphs called Sentiment Transformer Graph Convolutional Network (ST-GCN). To the best of our knowledge, this is the first study to model the sentiment corpus as a heterogeneous graph and learn document and word embeddings using the proposed sentiment graph transformer neural network. In addition, our model offers an easy mechanism to fuse node positional information for graph datasets using Laplacian eigenvectors. Extensive experiments on four standard datasets show that our model outperforms the existing state-of-the-art models. View Full-Text
Keywords: sentiment analysis; graph neural network; deep learning; NLP transformer sentiment analysis; graph neural network; deep learning; NLP transformer
Show Figures

Figure 1

MDPI and ACS Style

AlBadani, B.; Shi, R.; Dong, J.; Al-Sabri, R.; Moctard, O.B. Transformer-Based Graph Convolutional Network for Sentiment Analysis. Appl. Sci. 2022, 12, 1316. https://doi.org/10.3390/app12031316

AMA Style

AlBadani B, Shi R, Dong J, Al-Sabri R, Moctard OB. Transformer-Based Graph Convolutional Network for Sentiment Analysis. Applied Sciences. 2022; 12(3):1316. https://doi.org/10.3390/app12031316

Chicago/Turabian Style

AlBadani, Barakat, Ronghua Shi, Jian Dong, Raeed Al-Sabri, and Oloulade Babatounde Moctard. 2022. "Transformer-Based Graph Convolutional Network for Sentiment Analysis" Applied Sciences 12, no. 3: 1316. https://doi.org/10.3390/app12031316

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop