{"title":"酒店评论中的讽刺检测:一种多模态深度学习方法","authors":"Yang Liu, Maomao Chi, Qiong Sun","doi":"10.1108/jhtt-04-2023-0098","DOIUrl":null,"url":null,"abstract":"Purpose\nThis study aims to detect consumer sarcasm through inconsistencies in sentiment features between text and images of hotel reviews.\n\nDesign/methodology/approach\nThis paper proposes a model for sarcasm detection based on multimodal deep learning using reviews of three hotel brands collected from two travel platforms, which can identify emotional inconsistencies within a modality and across modalities. Text-image interaction information is explored using graph neural networks (GNN) to detect essential clues in sarcasm sentiment.\n\nFindings\nThe research results show that the multimodal deep learning model outperforms other baseline models, which can help to understand hotel service evaluation and provide hotel managers with decision-making opinions.\n\nOriginality/value\nThis research can help hoteliers in two ways: detecting service quality and formulating strategies. By selecting reference hotel brands, hoteliers can better assess their level of service quality (optimal resource allocation ensues); therefore, sarcasm detection research is not only beneficial for hotel managers seeking to improve service quality. The multimodal deep learning method introduced in the present study can be replicated in other industries to help travel platforms optimize their products and services.\n","PeriodicalId":51611,"journal":{"name":"Journal of Hospitality and Tourism Technology","volume":null,"pages":null},"PeriodicalIF":5.3000,"publicationDate":"2024-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Sarcasm detection in hotel reviews: a multimodal deep learning approach\",\"authors\":\"Yang Liu, Maomao Chi, Qiong Sun\",\"doi\":\"10.1108/jhtt-04-2023-0098\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Purpose\\nThis study aims to detect consumer sarcasm through inconsistencies in sentiment features between text and images of hotel reviews.\\n\\nDesign/methodology/approach\\nThis paper proposes a model for sarcasm detection based on multimodal deep learning using reviews of three hotel brands collected from two travel platforms, which can identify emotional inconsistencies within a modality and across modalities. Text-image interaction information is explored using graph neural networks (GNN) to detect essential clues in sarcasm sentiment.\\n\\nFindings\\nThe research results show that the multimodal deep learning model outperforms other baseline models, which can help to understand hotel service evaluation and provide hotel managers with decision-making opinions.\\n\\nOriginality/value\\nThis research can help hoteliers in two ways: detecting service quality and formulating strategies. By selecting reference hotel brands, hoteliers can better assess their level of service quality (optimal resource allocation ensues); therefore, sarcasm detection research is not only beneficial for hotel managers seeking to improve service quality. The multimodal deep learning method introduced in the present study can be replicated in other industries to help travel platforms optimize their products and services.\\n\",\"PeriodicalId\":51611,\"journal\":{\"name\":\"Journal of Hospitality and Tourism Technology\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":5.3000,\"publicationDate\":\"2024-05-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Hospitality and Tourism Technology\",\"FirstCategoryId\":\"91\",\"ListUrlMain\":\"https://doi.org/10.1108/jhtt-04-2023-0098\",\"RegionNum\":3,\"RegionCategory\":\"管理学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"HOSPITALITY, LEISURE, SPORT & TOURISM\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Hospitality and Tourism Technology","FirstCategoryId":"91","ListUrlMain":"https://doi.org/10.1108/jhtt-04-2023-0098","RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"HOSPITALITY, LEISURE, SPORT & TOURISM","Score":null,"Total":0}
Sarcasm detection in hotel reviews: a multimodal deep learning approach
Purpose
This study aims to detect consumer sarcasm through inconsistencies in sentiment features between text and images of hotel reviews.
Design/methodology/approach
This paper proposes a model for sarcasm detection based on multimodal deep learning using reviews of three hotel brands collected from two travel platforms, which can identify emotional inconsistencies within a modality and across modalities. Text-image interaction information is explored using graph neural networks (GNN) to detect essential clues in sarcasm sentiment.
Findings
The research results show that the multimodal deep learning model outperforms other baseline models, which can help to understand hotel service evaluation and provide hotel managers with decision-making opinions.
Originality/value
This research can help hoteliers in two ways: detecting service quality and formulating strategies. By selecting reference hotel brands, hoteliers can better assess their level of service quality (optimal resource allocation ensues); therefore, sarcasm detection research is not only beneficial for hotel managers seeking to improve service quality. The multimodal deep learning method introduced in the present study can be replicated in other industries to help travel platforms optimize their products and services.
期刊介绍:
The Journal of Hospitality and Tourism Technology is the only journal dedicated solely for research in technology and e-business in tourism and hospitality. It is a bridge between academia and industry through the intellectual exchange of ideas, trends and paradigmatic changes in the fields of hospitality, IT and e-business. It covers: -E-Marketplaces, electronic distribution channels, or e-Intermediaries -Internet or e-commerce business models -Self service technologies -E-Procurement -Social dynamics of e-communication -Relationship Development and Retention -E-governance -Security of transactions -Mobile/Wireless technologies in commerce -IT control and preparation for disaster -Virtual reality applications -Word of Mouth. -Cross-Cultural differences in IT use -GPS and Location-based services -Biometric applications -Business intelligence visualization -Radio Frequency Identification applications -Service-Oriented Architecture of business systems -Technology in New Product Development