Consumer reviews sentiment analysis based on CNN-BiLSTM

被引：0
作者：
Guo X. [1 ]
Zhao N. [1 ]
Cui S. [2 ]
机构：
[1] Institute of Systems Engineering, Dalian University of Technology, Dalian
[2] Institute of Information and Decision Technology, Dalian University of Technology, Dalian
来源：
| 1600年 / Systems Engineering Society of China卷 / 40期
基金：
中国国家自然科学基金;
关键词：
BiLSTM model; CNN model; Deep learning; Online reviews; Sentiment analysis;
D O I：
10.12011/1000-6788-2018-1890-11
中图分类号：
学科分类号：
摘要：
Nowadays, the sentiment analysis of online reviews of goods has become an important work that many businesses cannot ignore, which is of great significance for businesses to understand user preferences and can also provide directional guidance for the next improvement of related products. However, the traditional analysis methods have been unable to solve the problems of feature extraction and semantics understanding in sentiment analysis. Aiming at such problems, this paper proposes sentiment analysis of online reviews method based on CNN-BiLSTM, which can not only establish the sequential relationship like LSTM, but also describe local spatial characteristics like CNN. The experimental results on data sets of medical services, logistics express, financial services, tourist accommodation and food and beverage show that this method can effectively discriminate the emotional tendency of consumers’ online reviews and is more accurate than traditional machine learning algorithm in sentiment classification in this paper and the F1 value can reach 94.67%. Moreover, the experiments show that the method has good fields expansibility. © 2020, Editorial Board of Journal of Systems Engineering Society of China. All right reserved.
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收藏
页码：653 / 663
页数：10
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