A review of graph neural networks: concepts, architectures, techniques, challenges, datasets, applications, and future directions
Deep learning has seen significant growth recently and is now applied to a wide range of
conventional use cases, including graphs. Graph data provides relational information …
conventional use cases, including graphs. Graph data provides relational information …
A Review of Deep Learning Models for Twitter Sentiment Analysis: Challenges and Opportunities
Microblogging site Twitter (re-branded to X since July 2023) is one of the most influential
online social media websites, which offers a platform for the masses to communicate …
online social media websites, which offers a platform for the masses to communicate …
Graph neural networks for text classification: A survey
Text Classification is the most essential and fundamental problem in Natural Language
Processing. While numerous recent text classification models applied the sequential deep …
Processing. While numerous recent text classification models applied the sequential deep …
Target-level sentiment analysis for news articles
The rapid growth of social media, news sites, and blogs increases the opportunity to express
and share an opinion on the Internet. Researchers from different fields take advantage of …
and share an opinion on the Internet. Researchers from different fields take advantage of …
A scientific research topic trend prediction model based on multi‐LSTM and graph convolutional network
Predicting the development trend of future scientific research not only provides a reference
for researchers to understand the development of the discipline, but also provides support …
for researchers to understand the development of the discipline, but also provides support …
Text visualization for geological hazard documents via text mining and natural language processing
Y Ma, Z **e, G Li, K Ma, Z Huang, Q Qiu, H Liu - Earth Science Informatics, 2022 - Springer
An increasing number of geological hazard documents about the mechanism and
occurrence process of geological disasters contain unstructured geoscientific data that are …
occurrence process of geological disasters contain unstructured geoscientific data that are …
DCENet: A dynamic correlation evolve network for short-term traffic prediction
Graph neural networks (GNNs) have been extensively employed in traffic prediction tasks
due to their excellent capturing capabilities of spatial dependence. However, the majority of …
due to their excellent capturing capabilities of spatial dependence. However, the majority of …
Transformer-based graph convolutional network for sentiment analysis
B AlBadani, R Shi, J Dong, R Al-Sabri, OB Moctard - Applied Sciences, 2022 - mdpi.com
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 …
(NLP) and has attracted the attention of many researchers in the last few years. Recently …
Automated camera calibration via homography estimation with gnns
Over the past few decades, a significant rise of camera-based applications for traffic
monitoring has occurred. Governments and local administrations are increasingly relying on …
monitoring has occurred. Governments and local administrations are increasingly relying on …
Feature interactive graph neural network for KG-based recommendation
S Yan, C Li, H Wang, B Lin, Y Yuan - Expert Systems with Applications, 2024 - Elsevier
Graph neural network (GNN) is considered as the state-of-art method for KG-based
recommendation. However, the existing GNN-based recommendation methods …
recommendation. However, the existing GNN-based recommendation methods …