Home Appliance Review Analysis Via Adversarial Reptile

Tai Jung Kan, Chia Hui Chang

研究成果: 書貢獻/報告類型會議論文篇章同行評審

摘要

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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主出版物標題Proceedings - 2021 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, WI-IAT 2021
發行者Association for Computing Machinery
頁面64-70
頁數7
ISBN(電子)9781450391153
DOIs
出版狀態已出版 - 14 12月 2021
事件2021 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, WI-IAT 2021 - Virtual, Online, Australia
持續時間: 14 12月 202117 12月 2021

出版系列

名字ACM International Conference Proceeding Series

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???event.eventtypes.event.conference???2021 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, WI-IAT 2021
國家/地區Australia
城市Virtual, Online
期間14/12/2117/12/21

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