{"title":"从词汇到增长:从自然语言处理揭示政府对旅游业的关注","authors":"Lisi Yang , Yang Yang , Xijia Huang , Kai Yan","doi":"10.1016/j.tourman.2025.105264","DOIUrl":null,"url":null,"abstract":"<div><div>This paper theorizes and quantifies the government attention to tourism (GAT) using an AI-driven interdisciplinary approach to analyze government policy portfolios. By leveraging machine learning and natural language processing techniques, including textual analysis, word embeddings, and <em>GPT-4o-</em>based segmentation, the GAT indicator is derived from government annual reports. Within the framework of promotion tournament model and limited attention allocation theories, the study uses post-double-selection LASSO to identify key antecedents of GAT: the number of A-level scenic spots, male municipal party secretaries, and cities' economic constraints. These factors collectively shape government resource allocation in tourism policy. Validation tests confirm a positive association between GAT and actual government inputs in tourism-related domains. When governments' words align with actions, GAT can be a supplementary indicator for forecasting tourism growth. Robustness checks validate these findings, providing a reliable methodology. This study offers a comprehensive technology roadmap, guiding future tourism research with AI-driven approaches.</div></div>","PeriodicalId":48469,"journal":{"name":"Tourism Management","volume":"112 ","pages":"Article 105264"},"PeriodicalIF":10.9000,"publicationDate":"2025-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"From words to growth: Unveiling government attention to tourism from natural language processing\",\"authors\":\"Lisi Yang , Yang Yang , Xijia Huang , Kai Yan\",\"doi\":\"10.1016/j.tourman.2025.105264\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>This paper theorizes and quantifies the government attention to tourism (GAT) using an AI-driven interdisciplinary approach to analyze government policy portfolios. By leveraging machine learning and natural language processing techniques, including textual analysis, word embeddings, and <em>GPT-4o-</em>based segmentation, the GAT indicator is derived from government annual reports. Within the framework of promotion tournament model and limited attention allocation theories, the study uses post-double-selection LASSO to identify key antecedents of GAT: the number of A-level scenic spots, male municipal party secretaries, and cities' economic constraints. These factors collectively shape government resource allocation in tourism policy. Validation tests confirm a positive association between GAT and actual government inputs in tourism-related domains. When governments' words align with actions, GAT can be a supplementary indicator for forecasting tourism growth. Robustness checks validate these findings, providing a reliable methodology. This study offers a comprehensive technology roadmap, guiding future tourism research with AI-driven approaches.</div></div>\",\"PeriodicalId\":48469,\"journal\":{\"name\":\"Tourism Management\",\"volume\":\"112 \",\"pages\":\"Article 105264\"},\"PeriodicalIF\":10.9000,\"publicationDate\":\"2025-07-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Tourism Management\",\"FirstCategoryId\":\"91\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0261517725001347\",\"RegionNum\":1,\"RegionCategory\":\"管理学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENVIRONMENTAL STUDIES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Tourism Management","FirstCategoryId":"91","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0261517725001347","RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENVIRONMENTAL STUDIES","Score":null,"Total":0}
From words to growth: Unveiling government attention to tourism from natural language processing
This paper theorizes and quantifies the government attention to tourism (GAT) using an AI-driven interdisciplinary approach to analyze government policy portfolios. By leveraging machine learning and natural language processing techniques, including textual analysis, word embeddings, and GPT-4o-based segmentation, the GAT indicator is derived from government annual reports. Within the framework of promotion tournament model and limited attention allocation theories, the study uses post-double-selection LASSO to identify key antecedents of GAT: the number of A-level scenic spots, male municipal party secretaries, and cities' economic constraints. These factors collectively shape government resource allocation in tourism policy. Validation tests confirm a positive association between GAT and actual government inputs in tourism-related domains. When governments' words align with actions, GAT can be a supplementary indicator for forecasting tourism growth. Robustness checks validate these findings, providing a reliable methodology. This study offers a comprehensive technology roadmap, guiding future tourism research with AI-driven approaches.
期刊介绍:
Tourism Management, the preeminent scholarly journal, concentrates on the comprehensive management aspects, encompassing planning and policy, within the realm of travel and tourism. Adopting an interdisciplinary perspective, the journal delves into international, national, and regional tourism, addressing various management challenges. Its content mirrors this integrative approach, featuring primary research articles, progress in tourism research, case studies, research notes, discussions on current issues, and book reviews. Emphasizing scholarly rigor, all published papers are expected to contribute to theoretical and/or methodological advancements while offering specific insights relevant to tourism management and policy.