Lifelong Machine Learning-Based Quality Analysis for Product Review

Xianbin Hong, Sheng Uei Guan, Prudence W.H. Wong, Nian Xue, Ka Lok Man, Dawei Liu, Zhen Li

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review

2 Citations (Scopus)

Abstract

Reading product reviews is the best way to know the product quality in online shopping. Due to the huge review number, customers and merchants need product analysis algorithms to help with quality analysis. Current researches use sentiment analysis to replace quality analysis. However, it has a significant drawback. This paper proves that the sentiment-based analysis algorithms are insufficient for online product quality analysis. They ignore the relationship between aspect and its description and cannot detect noise (unrelated description). So this paper raises a Lifelong Product Quality Analysis algorithm LPQA to learn the relationship between aspects. It can detect the noise and improve the opinion classification performance. It improves the classification F1 score to 77.3% on the Amazon iPhone dataset and 69.99% on Semeval Laptop dataset.

Original languageEnglish
Title of host publication2021 3rd International Conference on Advanced Information Science and System, AISS 2021 - Conference Proceedings
PublisherAssociation for Computing Machinery
ISBN (Electronic)9781450385862
DOIs
Publication statusPublished - 26 Nov 2021
Event3rd International Conference on Advanced Information Science and System, AISS 2021 - Sanya, China
Duration: 26 Nov 202128 Nov 2021

Publication series

NameACM International Conference Proceeding Series

Conference

Conference3rd International Conference on Advanced Information Science and System, AISS 2021
Country/TerritoryChina
CitySanya
Period26/11/2128/11/21

Keywords

  • Expert System
  • Knowledge Base
  • Lifelong Machine Learning

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