Perturbed Masking for aspect-based sentiment analysis
Subject Areas : Journal of Computer & RoboticsAkarm Karimi zarandi 1 , Sayeh Mirzaei 2
1 - School of Engineering Science, College of Engineering, University of Tehran, Iran
2 - School of Engineering Science, College of Engineering, University of Tehran, Iran
Keywords: Aspect-based sentiment analysis, Deep Learning, Transformer, Perturbed Masking, Bert.,
Abstract :
Aspect based sentiment analysis (ABSA) has gained considerable attention due to its wide range of applications in commercial, social media, and political intelligence, allowing organizations to gain valuable insights from customer opinions and public discourse. This field focuses on analyzing texts from different perspectives, specifically identifying sentiments associated with various aspects of a product or service, making it essential to enhance the performance of ABSA techniques. Various approaches, including lexicon based methods, machine learning, and hybrid models, have been explored to tackle the complexities involved in this task. In particular, the integration of deep learning and transformer networks has shown promising results, especially in capturing nuanced language patterns. In this study, we propose a model that leverages pre trained language models, incorporating the Perturbed Masking method to capture both syntactic and semantic information, thus enabling a more comprehensive analysis. Our experiments, conducted on the SemEval 2014 dataset (restaurant and laptop reviews) and the Twitter dataset, demonstrate the model’s robust performance in improving ABSA results, offering new possibilities for more accurate sentiment prediction across diverse domains.
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