Title: Data mining model based on user reviews and star ratings

Authors: Yusong Cheng; Lei Lyu; Jin Wenxin; Chenhui Wang

Addresses: Business School, Hohai University, 200 Jinling North Road, Changzhou, Jiangsu Province, China ' Business School, Hohai University, 200 Jinling North Road, Changzhou, Jiangsu Province, China ' Business School, Hohai University, 200 Jinling North Road, Changzhou, Jiangsu Province, China ' Business School, Hohai University, 200 Jinling North Road, Changzhou, Jiangsu Province, China

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.

Keywords: LSTM; long short-term memory; boot-strapping; word2vec; aspect-level sentiment analysis; comment text; user online reviews; star ratings; online review helpfulness.

DOI: 10.1504/IJHPSA.2020.111561

International Journal of High Performance Systems Architecture, 2020 Vol.9 No.2/3, pp.107 - 116

Received: 08 May 2020
Accepted: 25 Jun 2020
Published online: 01 Dec 2020 *

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