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| Detecting Sarcasm from Travel Reviews Based on Cross-Modal Deep Learning |
Liu Yang1,2( ),Ma Lili3,Zhang Wen1,Hu Zhongyi1,2,Wu Jiang1,2 |
1School of Information Management, Wuhan University, Wuhan 430072,China
2Center for E-commerce Research and Development, Wuhan University, Wuhan 430072, China
3Economics and Management School, Wuhan University, Wuhan 430072, China |
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Abstract [Objective] Based on the cross-modal deep learning method, this paper analyzes consumers’ sentiments in travel reviews and identifies their sarcastic expression. [Methods] First, we encoded multi-modal information. Then, we extracted the interaction information between texts and pictures with the graph neural network. Finally, we used the attention mechanism to identify multi-modal features and sarcasm. [Results] We examined the proposed model with travel reviews from Yelp. The accuracy of sarcasm detection reached 88.77%, which is better than the baseline models. [Limitations] We only examined the proposed model with reviews on Hilton hotels, which needs to be expanded in the future. [Conclusions] The proposed model could extract interaction information between different modal of data, that effectively improve the accuracy of sarcasm detection.
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Received: 06 April 2022
Published: 03 February 2023
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| Fund:National Key R&D Program of China(2019YFB1405600);National Natural Science Foundation of China(72171183);National Natural Science Foundation of China(72204190) |
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Corresponding Authors:
Liu Yang，ORCID：0000-0002-9410-1755
E-mail: yang.liu27@whu.edu.cn
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