Chinese review sentiment analysis based on deep learning and attention mechanism

Authors

  • Wenjie Hu

DOI:

https://doi.org/10.54097/jceim.v10i1.5073

Keywords:

Sentiment classification, Convolutional neural networks, Long short-term memory networks, Attention mechanism

Abstract

With the development of the Internet and the continuous growth of the number of netizens, more and more people publish their attitudes and emotions on products, services, etc. on the Internet, accumulating a large number of comments containing personal opinions. The sentiment analysis of text reviews is of great significance to explore the emotional tendencies of users and help merchants adjust product positioning and marketing strategies in a timely manner. The deep learning framework adopts CNN and LSTM models respectively to introduce the attention mechanism for sentiment classification. The effects of CNN model and LSTM model on sentiment classification in Weibo comments were analyzed. From the experimental data, after the same number of iterations, the accuracy of the CNN model reaches 97.94%, and the accuracy of the LSTM model reaches 98.18%. The experimental results show that LSTM is better than CNN in the sentiment classification of Weibo comments.

References

Yang Shuxin, Zhang Nan Text sentiment analysis combining sentiment dictionary and contextual language model[J]. Journal of Computer Applications, 2021, 41(10): 2829-2834.

XU Minlin. Research on text sentiment analysis combining sentiment dictionary and neural network[D] Jiangxi University of Science and Technology, 2020.

WANG Zhiying. Research on text sentiment analysis based on Spark and machine learning[D] Central China Normal University, 2021.

Yan Junchao, Zhao Zhihao, Zhao Rui Research on Social Media Text Sentiment Analysis Based on Machine Learning[J]. Information and Computer (Theoretical Edition), 2019, 31(20): 44-47.

Xu Kangting, Song Wei A Chinese-text sentiment analysis method combining language knowledge and deep learning[J/OL] Big Data: 1-16[2022-05-04].

XIE Caiyun,HE Mingzhi,ZHOU Panfeng,PENG Lin,LI Xuanying Research on Text Sentiment Analysis Based on Deep Learning[J]. Information and Computer (Theoretical Edition), 2022, 34(03): 84-86.

WANG Hanqi. E-commerce platform review text sentiment analysis based on deep learning[D] Guilin University of Electronic Technology, 2021.

WU Yinggang. Research on text sentiment analysis based on deep learning[D] Guangdong University of Technology, 2021

ZHANG Tingting. Research on Chinese Review Text Sentiment Analysis Based on Deep Learning and Attention Mechanism[D]. Anhui Jianzhu University, 2021.

YAN Weichen. Research on text sentiment analysis based on deep learning[D] Harbin University of Science and Technology, 2021.

Downloads

Published

06-02-2023

Issue

Section

Articles

How to Cite

Hu, W. (2023). Chinese review sentiment analysis based on deep learning and attention mechanism. Journal of Computing and Electronic Information Management, 10(1), 8-11. https://doi.org/10.54097/jceim.v10i1.5073

Similar Articles

1-10 of 87

You may also start an advanced similarity search for this article.