Home Appliance Review Analysis Via Adversarial Reptile

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).