Sentiment analysis of low-carbon travel APP user comments based on deep learning

Xing Min Lin, Chun Heng Ho, Lu Ting Xia, Ruo Yi Zhao

研究成果: Article同行評審

摘要

Online car-hailing has become an important travel mode in today's society, and it is also an important part of the development of smart transportation. Optimizing online car-hailing consumer behavior preferences can promote low-carbon travel, thereby saving energy and reducing pollution. The use of text sentiment analysis methods is an important analytical approach for online ride-hailing APP review data. Traditional machine learning methods and artificially labeled sentiment dictionary methods rely too much on manual operations when performing sentiment analysis. This paper aims to reduce the dependence of sentiment analysis methods on humans and develop more efficient sentiment analysis methods. This study uses an experimental model based on the combination of long and short-term memory neural network (Bi LSTM) and attention mechanism (Attention). Our results verify that the emotion classification method combining the two has higher F1 value, average macro recall rate and average accuracy rate. It proves that the method of combining Attention and Bi LSTM is more accurate and efficient than traditional machine learning techniques.

原文English
文章編號101014
期刊Sustainable Energy Technologies and Assessments
44
DOIs
出版狀態Published - 2021 四月

All Science Journal Classification (ASJC) codes

  • Renewable Energy, Sustainability and the Environment
  • Energy Engineering and Power Technology

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