@inproceedings{3e765a48a95c42f5a77ebbf6cb5c73b3,
title = "Lifelong Machine Learning-Based Quality Analysis for Product Review",
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.",
keywords = "Expert System, Knowledge Base, Lifelong Machine Learning",
author = "Xianbin Hong and Guan, {Sheng Uei} and Wong, {Prudence W.H.} and Nian Xue and Man, {Ka Lok} and Dawei Liu and Zhen Li",
note = "Publisher Copyright: {\textcopyright} 2021 ACM.; 3rd International Conference on Advanced Information Science and System, AISS 2021 ; Conference date: 26-11-2021 Through 28-11-2021",
year = "2021",
month = nov,
day = "26",
doi = "10.1145/3503047.3503154",
language = "English",
series = "ACM International Conference Proceeding Series",
publisher = "Association for Computing Machinery",
booktitle = "2021 3rd International Conference on Advanced Information Science and System, AISS 2021 - Conference Proceedings",
}