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Systematic investigation of keywords selection and processing strategy on search engine forecasting: a case of tourist volume in Beijing
The timeliness, precision, and low cost of search data have great potential for projecting tourist volume. Obtaining valuable information for decision-making, particularly for predicting, is hampered by the vast amount of search data. A systematic investigation of keyword selection and processing ha...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9640785/ http://dx.doi.org/10.1007/s40558-022-00238-5 |
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author | Yuan, Ziqi Jia, Guozhu |
author_facet | Yuan, Ziqi Jia, Guozhu |
author_sort | Yuan, Ziqi |
collection | PubMed |
description | The timeliness, precision, and low cost of search data have great potential for projecting tourist volume. Obtaining valuable information for decision-making, particularly for predicting, is hampered by the vast amount of search data. A systematic investigation of keyword selection and processing has been conducted. Using Beijing tourist volume as an example, 11 different feature extraction algorithms were selected and combined with long short-term memory (LSTM), random forest (RF) and fuzzy time series (FTS) for forecasting tourist volume. A total of 1612 keywords were retrieved from Baidu Index demand mapping using the direct word extraction method, range word extraction method and empirical selection method. The remaining 813 keywords were subjected to feature extraction. Based on the forecasting results of medium and short-term (1-day, 7-days and 10-days), the forecasting results of Kernel principal component analysis (KPCA) and locally linear embedding (LLE) are relatively stable when the dimensionality is reduced to 5 dimensions. The forecasting results of t-stochastic neighbor embedding (t-SNE), isometric mapping (IsoMap) and locally linear embedding (LLE), locality preserving projections (LPP), independent component correlation (ICA) are relatively stable when the dimensionality is reduced to 10 dimensions. Accurately forecasting many factors (transportation, attraction, food, lodging, travel, tips, tickets, and weather) provides a solid foundation for tourism demand optimization and scientific management and a resource for tourists' holistic vacation planning. |
format | Online Article Text |
id | pubmed-9640785 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-96407852022-11-14 Systematic investigation of keywords selection and processing strategy on search engine forecasting: a case of tourist volume in Beijing Yuan, Ziqi Jia, Guozhu Inf Technol Tourism Case Study The timeliness, precision, and low cost of search data have great potential for projecting tourist volume. Obtaining valuable information for decision-making, particularly for predicting, is hampered by the vast amount of search data. A systematic investigation of keyword selection and processing has been conducted. Using Beijing tourist volume as an example, 11 different feature extraction algorithms were selected and combined with long short-term memory (LSTM), random forest (RF) and fuzzy time series (FTS) for forecasting tourist volume. A total of 1612 keywords were retrieved from Baidu Index demand mapping using the direct word extraction method, range word extraction method and empirical selection method. The remaining 813 keywords were subjected to feature extraction. Based on the forecasting results of medium and short-term (1-day, 7-days and 10-days), the forecasting results of Kernel principal component analysis (KPCA) and locally linear embedding (LLE) are relatively stable when the dimensionality is reduced to 5 dimensions. The forecasting results of t-stochastic neighbor embedding (t-SNE), isometric mapping (IsoMap) and locally linear embedding (LLE), locality preserving projections (LPP), independent component correlation (ICA) are relatively stable when the dimensionality is reduced to 10 dimensions. Accurately forecasting many factors (transportation, attraction, food, lodging, travel, tips, tickets, and weather) provides a solid foundation for tourism demand optimization and scientific management and a resource for tourists' holistic vacation planning. Springer Berlin Heidelberg 2022-11-07 2022 /pmc/articles/PMC9640785/ http://dx.doi.org/10.1007/s40558-022-00238-5 Text en © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | Case Study Yuan, Ziqi Jia, Guozhu Systematic investigation of keywords selection and processing strategy on search engine forecasting: a case of tourist volume in Beijing |
title | Systematic investigation of keywords selection and processing strategy on search engine forecasting: a case of tourist volume in Beijing |
title_full | Systematic investigation of keywords selection and processing strategy on search engine forecasting: a case of tourist volume in Beijing |
title_fullStr | Systematic investigation of keywords selection and processing strategy on search engine forecasting: a case of tourist volume in Beijing |
title_full_unstemmed | Systematic investigation of keywords selection and processing strategy on search engine forecasting: a case of tourist volume in Beijing |
title_short | Systematic investigation of keywords selection and processing strategy on search engine forecasting: a case of tourist volume in Beijing |
title_sort | systematic investigation of keywords selection and processing strategy on search engine forecasting: a case of tourist volume in beijing |
topic | Case Study |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9640785/ http://dx.doi.org/10.1007/s40558-022-00238-5 |
work_keys_str_mv | AT yuanziqi systematicinvestigationofkeywordsselectionandprocessingstrategyonsearchengineforecastingacaseoftouristvolumeinbeijing AT jiaguozhu systematicinvestigationofkeywordsselectionandprocessingstrategyonsearchengineforecastingacaseoftouristvolumeinbeijing |