Dual‐Level Attention Based on a Heterogeneous Graph Convolution Network for Aspect‐Based Sentiment Classification

Abstract

With the development of 5G, the advancement of basic infrastructure has led to considerable development in related research and technology. It also promotes the development of various smart devices and social platforms. More and more people are now using smart devices to post their reviews right after something happens. In order to keep pace with this trend, we propose a method to analyze users’ sentiment by using their text data. When analyzing users’ text data, it is noted that a user’s review may contain many aspects. Traditional text classification methods used by smart devices, however, usually ignore the importance of multiple aspects of a review. Additionally, most algorithms usually ignore the network structure information between the words in a sentence and the sentence itself. To address these issues, we propose a novel dual-level attention-based heterogeneous graph convolutional network for aspect-based sentiment classification which minds more context information through information propagation along with graphs. Particularly, we first propose a flexible HIN (heterogeneous information network) framework to model the user-generated reviews. This framework can integrate various types of additional information and capture their relationships to alleviate semantic sparsity of some labeled data. This framework can also leverage the full advantage of the hidden network structure information through information propagation along with graphs. Then, we propose a dual-level attention-based heterogeneous graph convolutional network (DAHGCN), which includes node-level and type-level attentions. The attention mechanisms can analyze the importance of different adjacent nodes and the importance of different types of nodes for the current node. The experimental results on three real-world datasets demonstrated the effectiveness and reliability of our model.

A Novel Maximum Flow Algorithm with Neural Network for Time-Varying Wastage Networks

“…e maximum flow problem (MFP) is a classical network optimization problem [1,2], and it is also a general problem in vehicular networks [3], network optimization [4], social network [5,6], and other fields. Ford and Fulkerson first solved the maximum flow problem using the labeling algorithm (viz.…”

A Comprehensive Review on Artificial Intelligence/Machine Learning Algorithms for Empowering the Future IoT Toward 6G Era

“…Research topic Research analysis/findings Deep Learning [148] Network access and routing algorithm Survey on DL, supervised, reinforcement and imitation learning [149], [150] Indoor localization Localization error analysis [151] CSI estimation technique CSI overhead, channel measurement and sum rate analysis [152] DoA estimation Estimation accuracy analysis with the proposed, RVNN, SVR and MUSIC approaches [154] Power allocation strategy Analysis of secrecy rate, computation time and interference leakage [155] QoE forecasting mechanism Performance analysis of the proposed scheme against SVR, MLP, LSTM-based schemes [156] Anti-jamming scheme Throughput analysis Transformer algorithm [157] Medical image classification Classification accuracy analysis [158] Traffic sign recognition Classification accuracy analysis [159] Wildfire recognition and region detection Classification and detection accuracy analysis [160] Modulation recognition Classification and detection accuracy analysis [161] Intrusion detection Detection accuracy analysis Graph neural network [162] Topology control Network lifetime enhancement [163] IoT device tracking Tracking optimization in terms of execution time and distance covered by the tracking devices [164] Sentiment classification Interpretation accuracy of the aspect of text(s) [165] Vehicular traffic data prediction Prediction accuracy of the missing data from the available dataset recognition mechanism is designed in [158] with the help of DNN consisting of CNN and transformer-based algorithm.…”

“…This model, to be potentially implemented in 6G communication systems, is analyzed in terms of time taken and distance travelled by the UAV's to reach the target. In case of smart application sectors, a GNN-based sentiment classification algorithm is designed in [164] to interpret the aspect of a text. In [165], GNN is used to develop a prediction mechanism for smart transportation system which can predict missing information about the traffic data from a given dataset.…”

Chinese community topic classification method based on graph model

“…Zhang [13] proposed TextING to update node information using Gated GNN [14]. Literatures [15][16] introduce other models to alleviate the sparsity problem, but increase the model complexity. In addition, all of the above methods ignore the importance of key information to the text when building graph models, and construct a single graph by treating all words equally, increasing the influence of irrelevant data.…”