Citation

Muhammad Azman Busst, Mikail and Sonai Muthu Anbananthen, Kalaiarasi and Kannan, Subarmaniam and Kannan, Rajkumar (2024) Extracting Explicit and Implicit Aspects Using Deep Learning. Emerging Science Journal, 8 (1). pp. 61-76. ISSN 2610-9182

Text Extracting Explicit and Implicit Aspects Using Deep Learning _ Azman Busst _ Emerging Science Journal.pdf - Published Version Restricted to Repository staff only Download (2MB)

Abstract

The proliferation of user-generated content on social networks and websites has heightenedthe significance of sentiment analysis, also known as opinion mining, as a critical tool for comprehending people’s attitudes toward various topics. Aspect-level sentiment analysis, which considers specific aspects or features of texts, provides a more comprehensive view of sentiment analysis. The aspect-level approach encompasses both explicit and implicit aspects, where explicit aspects are readily mentioned in texts while implicit aspects are implied or inferred from contextual clues. Despite the significance of implicit aspects in the overall review, previous research has predominantly focused on explicit aspect extraction. Limited attention has been given to the extraction of implicit aspects, despite their potential impact on capturing the complete sentiment picture of texts. Therefore, this study aims to find an aspect extraction solution capable of identifying and extracting both explicit and implicit aspects from texts. This study compares various machine and deep learning models on the SemEval-2014and SemEval-2016 restaurant datasets. The experimental analysis demonstrates that the proposed Aspect-BiLSTM model emerged as the best-performing model, achieving high accuracy in classifying both explicit and implicit aspects, with 92.9% accuracy for the2014 and 90.7% accuracy for the 2016 datasets. Notably, the proposed solution was able to capture multiple aspects of texts, making it more robust and versatile. This study highlights the efficacy of the Aspect-BiLSTM model for aspect extraction, which will give valuable insights into the advancement of aspect-level sentiment analysis

Item Type:	Article
Uncontrolled Keywords:	Deep Learning
Subjects:	Q Science > Q Science (General) > Q300-390 Cybernetics
Divisions:	Faculty of Information Science and Technology (FIST)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	03 Apr 2024 00:21
Last Modified:	03 Apr 2024 00:21
URII:	http://shdl.mmu.edu.my/id/eprint/12287

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