| Autor: |
Cheng, Yusong, Lyu, Lei, Wenxin, Jin, Wang, Chenhui |
| Zdroj: |
International Journal of High Performance Systems Architecture; 2020, Vol. 9 Issue: 1 p107-116, 10p |
| Abstrakt: |
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. |
| Databáze: |
Supplemental Index |
| Externí odkaz: |
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