Improving Aspect-Based Sentiment Analysis in E-Commerce Reviews Using Multi-Head Attention Graph Convolutional Networks

International Journal of Computer Science and Technology | Vol. 3, No. 4, April 2022 | pp. 25–32

Research Article

Title: Improving Aspect-Based Sentiment Analysis in E-Commerce Reviews Using Multi-Head Attention Graph Convolutional Networks

Names of Authors: Siti Aminah¹, Budi Santoso², and Agus Wijaya³

Authors’ Affiliations:
¹Department of Computer Science and Information Systems, Universitas Indonesia, Jakarta, Indonesia
²School of Electrical Engineering and Informatics, Bandung Institute of Technology, Bandung, Indonesia
³Department of Informatics, Sepuluh Nopember Institute of Technology, Surabaya, Indonesia

Abstract: Aspect-Based Sentiment Analysis (ABSA) is a vital text-mining task that enables e-commerce companies to extract detailed consumer feedback by identifying specific product aspects and their associated emotional tones. However, context sentences often present complex grammatical structures and multi-word feature expressions that distance an aspect from its descriptor words, confusing standard recurrent neural network models. This study proposes a novel ABSA framework that utilizes a multi-head self-attention mechanism integrated with a Graph Convolutional Network (GCN). The system uses a dependency parser to convert an input text review into a syntactic dependency graph, mapping structural links between individual tokens. The GCN layer operates directly on this graph structure, extracting syntax-aware word features, while the multi-head self-attention module captures long-range semantic dependencies across the sentence layout. Experiments conducted on standard consumer electronic evaluation datasets demonstrate that the proposed attention-driven GCN model achieves an overall classification accuracy of 86.4% and an F1-score of 79.8%. These metrics represent a significant performance gain over baseline LSTM and standard BERT sequence classification systems, demonstrating high reliability in extracting consumer sentiments from complex, informal text structures.

Keywords: Aspect-based sentiment analysis, Natural language processing, Graph convolutional networks, Attention mechanisms, Text mining, E-commerce feedback

Manuscript Timeline: Received: January 14, 2022; Revised: February 22, 2022; Accepted: March 18, 2022; Published: April 1, 2022

Citation: Aminah, S., Santoso, B., & Wijaya, A. (2022). Improving aspect-based sentiment analysis in e-commerce reviews using multi-head attention graph convolutional networks. International Journal of Computer Science and Technology, 3(4), 25–32. DOI: 10.46882/2022/IJCST/000028

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