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数据分析与知识发现  2022, Vol. 6 Issue (12): 23-31     https://doi.org/10.11925/infotech.2096-3467.2022.0308
  研究论文 本期目录 | 过刊浏览 | 高级检索 |
基于跨模态深度学习的旅游评论反讽识别*
刘洋1,2(),马莉莉3,张雯1,胡忠义1,2,吴江1,2
1武汉大学信息管理学院 武汉 430072
2武汉大学电子商务研究与发展中心 武汉 430072
3武汉大学经济与管理学院 武汉 430072
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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摘要 

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。

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刘洋
马莉莉
张雯
胡忠义
吴江
关键词 跨模态深度学习旅游评论反讽识别    
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.

Key wordsCross-Modal    Deep Learning    Travel Reviews    Sarcasm Detection
收稿日期: 2022-04-06      出版日期: 2023-02-03
ZTFLH:  TP391  
  G350  
基金资助:*国家重点研发计划(2019YFB1405600);国家自然科学基金项目(72171183);国家自然科学基金青年项目(72204190)
通讯作者: 刘洋,ORCID:0000-0002-9410-1755     E-mail: yang.liu27@whu.edu.cn
引用本文:   
刘洋, 马莉莉, 张雯, 胡忠义, 吴江. 基于跨模态深度学习的旅游评论反讽识别*[J]. 数据分析与知识发现, 2022, 6(12): 23-31.
Liu Yang, Ma Lili, Zhang Wen, Hu Zhongyi, Wu Jiang. Detecting Sarcasm from Travel Reviews Based on Cross-Modal Deep Learning. Data Analysis and Knowledge Discovery, 2022, 6(12): 23-31.
链接本文:  
https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/10.11925/infotech.2096-3467.2022.0308      或      https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/Y2022/V6/I12/23
Fig.1  旅游评论中反讽的例子
Fig.2  跨模态反讽识别模型
模态 模型 Accuracy/% Precision/% Recall/% F1-Score/%
文本模态 TextCNN 82.93 83.45 84.65 82.22
Bi-LSTM 84.15 84.26 84.17 85.58
BERT 82.14 81.33 82.45 82.68
SVM+BERT 84.98 85.43 84.79 85.25
图像模态 ResNet 72.55 74.88 73.64 71.09
EfficientNet 78.02 77.23 75.15 76.34
UPB-MTL 79.41 78.43 78.92 78.45
多模态 BERT+ResNet 84.75 84.02 84.65 83.82
Attention+BERT+ResNet 86.46 84.18 85.32 85.93
本文模型 88.77 86.34 87.42 87.86
Table 1  算法性能对比结果
Fig.3  不同模型的结果对比
模态 模型 Accuracy/% Precision/% Recall/% F1-score/%


-Attention 86.85 85.13 85.80 86.56
-GCN 83.29 84.54 83.48 84.07
本文 88.77 86.34 87.42 87.86
Table 2  消融实验的结果
游客ID 时间 评论文本 图片 正确
标签
实际分类标签
Alex P 2021.1.29 We stayed at Hilton Palace for one night while we stopped and enjoyed Disney Springs while we drove through Florida. For the price we paid, I'm disappointed. The room was tiny, outdated, sliding window was must have had the gases released through the window panes that it fogged it completely. Slept on a mattress cover and not sheets. which was weird. I wouldn't stay here again. I guess I expected more out of Hilton across from Disney World.
0

1
Christina Z 2021.8.6 I was here for work. There was a convention at the Orange County Convention Center. The hotel was very clean. Covid rules were very relaxed, but I expected that in Florida. Their room service is still suspended but their restaurant David's does take out. The staff was extremely friendly. Their WiFi was awful in the rooms and made it challenging to do work. Likewise the TV would keep going in and out. We weren't having any storms so I'm not sure what the problem was. I would stay here again. I just hope they fix their internet
0

1
Table 3  错误分类的旅游评论
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