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Home Appliance Review Analysis Via Adversarial Reptile

Published: 13 April 2022 Publication History

Abstract

Studying discussion of products on social media can help manufacturers improve their products. Opinions provided through online reviews can immediately reflect whether the product is accepted by people, and which aspects of the product are most discussed. In this article, we divide the analysis of home appliances into three tasks, including named entity recognition (NER), aspect category extraction (ACE), and aspect category sentiment classification (ACSC). To improve the performance of ACSC, we combine the Reptile algorithm in meta learning with the concept of domain adversarial training to form the concept of the Adversarial Reptile algorithm. We found that the macro-F1 is improved from 68.6% (BERT fine-tuned model) to 70.3% (p-value 0.04).

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cover image ACM Conferences
WI-IAT '21: IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology
December 2021
698 pages
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Published: 13 April 2022

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Author Tags

  1. Meta Learning
  2. Sentiment Analysis
  3. Transfer Learning
  4. adversarial Training

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WI-IAT '21
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WI-IAT '21: IEEE/WIC/ACM International Conference on Web Intelligence
December 14 - 17, 2021
VIC, Melbourne, Australia

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