Wenmiao Yang
ABSTRACT
By using convolutional neural network models, it is possible to quickly and accurately recommend potential tourist attractions and routes of interest to users based on their interests and behaviors, thereby significantly improving the user experience. This article is based on a convolutional neural network model and develops a system that can recommend suitable tourist attractions through users uploading videos and capturing facial images for intelligent sentiment analysis. The experimental results show that the accuracy of the model reaches 83.3%, proving that it can accurately recognize user emotions and provide a more suitable travel recommendation scheme. In the post pandemic era, this system has extremely important practical significance in alleviating social anxiety and relieving people's daily depression.
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Index Terms
- Research and Application of a Tourism Recommendation System Based on Emotional Analysis
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