Article
Version 1
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Adversarial Fine-Grained Sentiment Analysis
Version 1
: Received: 7 April 2024 / Approved: 8 April 2024 / Online: 8 April 2024 (14:59:11 CEST)
How to cite: Rossi, G.; Al-Masri, A.; Nasir, W.; Watanabe, Y. Adversarial Fine-Grained Sentiment Analysis. Preprints 2024, 2024040568. https://doi.org/10.20944/preprints202404.0568.v1 Rossi, G.; Al-Masri, A.; Nasir, W.; Watanabe, Y. Adversarial Fine-Grained Sentiment Analysis. Preprints 2024, 2024040568. https://doi.org/10.20944/preprints202404.0568.v1
Abstract
The digital era has significantly amplified the volume of online reviews, presenting both opportunities and challenges in harnessing insights from consumer feedback. Aspect-Based Sentiment Analysis (ABSA) has emerged as a crucial tool for distilling sentiments from these reviews, providing valuable data for enhancing product and service quality. This study introduces an advanced hybrid model, AdvSentiNet, which integrates adversarial training into the state-of-the-art framework to elevate the precision of sentiment detection at the aspect level. By employing an adversarial network, where a generative model competes against a classifier by crafting highly realistic synthetic samples, we aim to bolster the model's resilience against varied data samples. This innovative approach, unexplored in its entirety within the realm of ABSA, demonstrated remarkable performance improvements on benchmark datasets. For instance, accuracy on the SemEval 2015 dataset escalated from 81.7% to 82.5%, and for the SemEval 2016 dataset, it surged from 84.4% to 87.3%.
Keywords
Aspect-Based Sentiment Analysis, Adversarial Training, Sentiment Detection
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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