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Deep Learning and Machine Learning Techniques for Analyzing Travelers' Online Reviews: A Review

Deep Learning and Machine Learning Techniques for Analyzing Travelers' Online Reviews: A Review
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Author(s): Elliot Mbunge (University of Eswatini, Eswatini) and Benhildah Muchemwa (University of Eswatini, Eswatini)
Copyright: 2022
Pages: 20
Source title: Optimizing Digital Solutions for Hyper-Personalization in Tourism and Hospitality
Source Author(s)/Editor(s): Nuno Gustavo (Estoril Higher Institute for Tourism and Hotel Studies, Portugal), João Pronto (Estoril Higher Institute for Tourism and Hotel Studies, Portugal), Luísa Carvalho (Polytechnic Institute of Setúbal, Portugal & and CEFAGE, University of Évora, Portugal) and Miguel Belo (Lisbon University, Portugal & Portuguese National Funding Agency for Science, Research and Technology, Portugal)
DOI: 10.4018/978-1-7998-8306-7.ch002

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Abstract

Social media platforms play a tremendous role in the tourism and hospitality industry. Social media platforms are increasingly becoming a source of information. The complexity and increasing size of tourists' online data make it difficult to extract meaningful insights using traditional models. Therefore, this scoping and comprehensive review aimed to analyze machine learning and deep learning models applied to model tourism data. The study revealed that deep learning and machine learning models are used for forecasting and predicting tourism demand using data from search query data, Google trends, and social media platforms. Also, the study revealed that data-driven models can assist managers and policymakers in mapping and segmenting tourism hotspots and attractions and predicting revenue that is likely to be generated, exploring targeting marketing, segmenting tourists based on their spending patterns, lifestyle, and age group. However, hybrid deep learning models such as inceptionV3, MobilenetsV3, and YOLOv4 are not yet explored in the tourism and hospitality industry.

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