基于跨模态深度学习的旅游评论反讽识别

Detecting Sarcasm from Travel Reviews Based on Cross-Modal Deep Learning

【目的】基于跨模态深度学习方法,通过旅游评论对消费者情感表达进行分析,并识别反讽情绪。【方法】构建跨模态的深度学习模型,首先进行多模态信息的编码,通过图神经网络提取文本与图片中的交互信息,利用注意力机制强调多模态特征,最后进行反讽识别。【结果】结合Yelp网站的旅游评论数据进行实证研究,并与相关基线模型作比较。实验结果表明,跨模态模型具有优越性,反讽识别的准确率达到88.77%。【局限】所提模型仅在Yelp网站的Hilton数据集上进行测试,未在其他旅游平台上进一步验证。【结论】所提模型能够充分提取不同模态间的交互信息,有效提升反讽识别的准确性。

[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.