Data mining model based on user reviews and star ratings. (23rd November 2020)

• Record Type: Journal Article
• Authors: Cheng, Yusong; Lyu, Lei; Wenxin, Jin; Wang, Chenhui
• Is Part Of: International journal of high performance systems architecture. Volume 9:Number 2/3(2020)
• Page Start: 107
• Page End: 116
• Publication Date: 2020-11-23
• Subjects: LSTM -- long short-term memory -- boot-strapping -- word2vec -- aspect-level sentiment analysis -- comment text -- user online reviews -- star ratings -- online review helpfulness; Computer architecture -- Periodicals; Computer systems -- Periodicals; High performance computing -- Periodicals; 004.205
• Languages: English
• ISSNs: 1751-6528

Abstract

With the rapid development of e-commerce, the research on sentiment analysis of online reviews has been paid more and more attention. This paper presents an Aspect-Level Sentiment Analysis Method based on long short-term memory (LSTM) and boot-strapping, which performs semantic mining and prediction on time-based data patterns and data combinations of text, star rating and helpful votes. A high prediction accuracy rate is obtained in the open data set. Compared with the traditional methods, which single analysis comment or evaluation, merchants can gain a deeper understanding of user feedback from sentiment analysis.