Time-based sampling methods for detecting helpful reviews

Ristu Saptono, Tsunenori Mine

研究成果: Chapter in Book/Report/Conference proceedingConference contribution

抄録

Product reviews describe customer opinions and experiences to products. Better opinions and experiences in the reviews more attract and help people who want to buy the products. The reviews, including such factors, are called helpful reviews. Many studies have been conducted to detect helpful reviews and proposed many useful factors, such as review-related factors, product-related factors, and reviewer-related factors. Meanwhile, the elapsed time of reviews has been used as a factor in detecting helpful reviews but never considered as sampling methods, despite that it is an essential factor to determine the freshness of the reviews, which influence the people being interested in the product. In this paper, we propose time-based sampling methods, which determine the sample size as small as possible in detecting helpful reviews with high accuracy. To investigate the effect of the time-based sampling methods in detecting helpful reviews, we conducted extensive experiments comparing with total sampling and simple random sampling, using two machine learning methods: XGBoost and CNN which involve text and numerical factors. Experimental results illustrate the validity of the proposed methods. Significantly, in large datasets, our proposed sampling methods outperform the other sampling methods.

本文言語英語
ホスト出版物のタイトルProceedings - 2020 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology, WI-IAT 2020
編集者Jing He, Hemant Purohit, Guangyan Huang, Xiaoying Gao, Ke Deng
出版社Institute of Electrical and Electronics Engineers Inc.
ページ508-513
ページ数6
ISBN(電子版)9781665419246
DOI
出版ステータス出版済み - 12 2020
イベント2020 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology, WI-IAT 2020 - Virtual, Online
継続期間: 12 14 202012 17 2020

出版物シリーズ

名前Proceedings - 2020 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology, WI-IAT 2020

会議

会議2020 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology, WI-IAT 2020
CityVirtual, Online
Period12/14/2012/17/20

All Science Journal Classification (ASJC) codes

  • 人工知能
  • コンピュータ ネットワークおよび通信
  • ソフトウェア

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