Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/107394
DC Field | Value | Language |
---|---|---|
dc.contributor | School of Hotel and Tourism Management | - |
dc.creator | Wu, DC | - |
dc.creator | Zhong, S | - |
dc.creator | Song, H | - |
dc.creator | Wu, J | - |
dc.date.accessioned | 2024-06-18T09:02:27Z | - |
dc.date.available | 2024-06-18T09:02:27Z | - |
dc.identifier.issn | 0278-4319 | - |
dc.identifier.uri | http://hdl.handle.net/10397/107394 | - |
dc.language.iso | en | en_US |
dc.publisher | Elsevier Ltd | en_US |
dc.subject | Hotel demand forecasting | en_US |
dc.subject | LDA topic modeling | en_US |
dc.subject | MIDAS model | en_US |
dc.subject | Online review text | en_US |
dc.subject | Sentiment analysis | en_US |
dc.title | Do topic and sentiment matter? Predictive power of online reviews for hotel demand forecasting | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.volume | 120 | - |
dc.identifier.doi | 10.1016/j.ijhm.2024.103750 | - |
dcterms.abstract | Studies integrating textual data for forecasting have mainly focused on the overall sentiment reflected in text. Yet textual data convey various types of information, such as review topics, that can be beneficial when forecasting hotel demand. This study aims to combine topic modeling and sentiment analysis to improve forecasting performance of hotel demand. Specifically, the latent Dirichlet allocation (LDA) topic modeling technique and the long short-term memory (LSTM) model are employed to construct topic-based sentiment indices. The autoregressive integrated moving average (ARIMA) with explanatory variable–type models and mixed data sampling (MIDAS) models are adopted for the evaluation of predictive power. Results reveal that MIDAS forecasting with topic–sentiment and COVID-19 variables generates most accurate forecasts. The findings contextualize the application of online textual big data in hotel demand forecasting research. Hotel management can utilize these online data for short-term forecasting to facilitate crowd management and respond more effectively to unforeseen public health events. | - |
dcterms.accessRights | embargoed access | en_US |
dcterms.bibliographicCitation | International journal of hospitality management, July 2024, v. 120, 103750 | - |
dcterms.isPartOf | International journal of hospitality management | - |
dcterms.issued | 2024-07 | - |
dc.identifier.scopus | 2-s2.0-85190865215 | - |
dc.identifier.eissn | 1873-4693 | - |
dc.identifier.artn | 103750 | - |
dc.description.validate | 202406 bcch | - |
dc.identifier.FolderNumber | a2835 | en_US |
dc.identifier.SubFormID | 48549 | en_US |
dc.description.fundingSource | Others | en_US |
dc.description.fundingText | the Guangdong Basic and Applied Basic Research Foundation; the National Natural Science Foundation of China; The Hong Kong Polytechnic University (ZJLP) | en_US |
dc.description.pubStatus | Published | en_US |
dc.date.embargo | 2027-07-31 | en_US |
dc.description.oaCategory | Green (AAM) | en_US |
Appears in Collections: | Journal/Magazine Article |
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