Using social network and semantic analysis to analyze online travel forums and forecast tourism demand

Colladon, Andrea Fronzetti; Guardabascio, Barbara; Innarella, Rosy

doi:10.1016/j.dss.2019.113075

Cited by 82 publications

(44 citation statements)

References 85 publications

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“…Online social networks connect millions of tourists while they are traveling and offer them services such as information, tour guides, accommodations, and so on. The use of social networks facilitates a large amount of data that is of interest to the tourism industry (French et al, 2017; Fronzetti Colladon et al, 2019; Gruss et al, 2019; J. H. Park et al, 2016).…”

Section: Citation Mappingmentioning

confidence: 99%

The Past, Present, and Future of Smart Tourism Destinations: A Bibliometric Analysis

Bastidas-Manzano

Sánchez‐Fernández²,

Casado‐Aranda

2020

Journal of Hospitality & Tourism Research

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Despite the emergence of the notion of smart tourism destinations in tourism research, few studies have delved deeply into the topic, and the little research focusing on the subject has not offered an overview of its evolution, current scope, and potential domains of interest. This study aims to fill this gap in literature by applying a bibliometric approach. The current research offers a comprehensive review of 258 investigations published between 2013 and 2019 in outlets indexed by the ISI Web of Science database and other relevant publications on information technology related to hospitality and tourism. The findings indicate the domains of tourism, journals, and themes that merit consideration in smart tourism destination future research and serve both professionals and academics as a guide for future lines to explore.

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Section: Citation Mappingmentioning

confidence: 99%

The Past, Present, and Future of Smart Tourism Destinations: A Bibliometric Analysis

Bastidas-Manzano

Sánchez‐Fernández²,

Casado‐Aranda

2020

Journal of Hospitality & Tourism Research

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“…The prediction of daily tourist flow at scenic destinations is essential to the tourism industry, and the accuracy of forecasting is highly significant for the optimal distribution of tourism resources [8,37,43]. Mountain Huangshan is a famous scenic spot in China, and its daily tourist volume is known to exhibit complex nonlinear characteristics and the historical tourist data exhibits various trends of fluctuation during different seasons [44].…”

Section: Discussionmentioning

confidence: 99%

“…Even price levels and web traffic have been as used as variables in certain studies [36]. User interactions on online forums have also been used to forecast tourist flows [37]. However, most of the methods are more suitable for long-term forecasting, rather than short-term forecasting.…”

Section: Introductionmentioning

confidence: 99%

Forecasting of Short-Term Daily Tourist Flow Based on Seasonal Clustering Method and PSO-LSSVM

Liang

et al. 2020

IJGI

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The accurate prediction of tourist flow is essential to appropriately prepare tourist attractions and inform the decisions of tourism companies. However, tourist flow in scenic spots is a dynamic trend with daily changes, and specialized methods are necessary to measure it accurately. For this purpose, a tourist flow forecasting method is proposed in this research based on seasonal clustering. The experiment employs the K-means algorithm considering seasonal variations and the particle swarm optimization-least squares support vector machine (PSO-LSSVM) algorithm to forecast the tourist flow in scenic spots. The LSSVM is also used to compare the performance of the proposed model with that of the existing ones. Experiments based on a dataset comprising the daily tourist data for Mountain Huangshan during the period between 2014 and 2017 are conducted. Our results show that seasonal clustering is an effective method to improve tourist flow prediction, besides, the accuracy of daily tourist flow prediction is significantly improved by nearly 3 percent based on the hybrid optimized model combining seasonal clustering. Compared with other algorithms which provide predictions at monthly intervals, the method proposed in this research can provide more timely analysis and guide professionals in the tourism industry towards better daily management.

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“…Data mining has emerged as an alternative tool for modeling and forecasting due to its ability to capture the non-linearity in the data. While the shortcoming of data mining is a large amount of training data [14], with the advent of big data era, data mining has recently been widely used for demand forecasting in the various fields, where data can be collected easily, such as energy [10,15], tourism [16,17], transportation [18][19][20], water management [21,22], remanufacturing [23], bike sharing [24,25], retail pharmacies [26], hospitals [27,28], logistics [14], and spare parts management [14,[29][30][31], showing its usefulness.…”

Section: Reviews On Related Workmentioning

confidence: 99%

Forecasting Spare Parts Demand of Military Aircraft: Comparisons of Data Mining Techniques and Managerial Features from the Case of South Korea

Choi¹,

Suh²

2020

Sustainability

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In a weapon system, the accurate forecasting of the spare parts demand can help avoid the excess inventory, leading to the efficient use of budget. It can also help develop the combat readiness of the weapon system by improving weapon system utilization. Moreover, as performance-based logistics (PBL) projects have recently emerged, the accurate demand forecasting of spare parts has become an important issue for the PBL contractors as well. However, for the demand forecasting of spare parts, the time series methods, typically used in the military sector, have low prediction accuracies and the PBL contractors are mostly based on the judgment of practitioners. Meanwhile, most of the previous studies in the military sector have not considered the managerial characteristics of spare parts (e.g., reparability and the irregularity of maintenance). No previous work has considered any such features, which can indicate the reliability of spare parts (e.g., mean time between failures (MTBF)), although they can affect the spare parts demand. Therefore, to develop a more accurate forecasting of the spare parts demand of military aircraft, we designed and examined a systematic approach that uses data mining techniques. To fill up the research gaps of related works, our approach also considered the managerial characteristics of spare parts and included the new features that represent the reliability of spare parts. Consequently, given the case of South Korea and the full feature set, we found random forest gave better results than the other data mining techniques and the conventional time series methods. Using the best technique Random Forest, we identified the contribution of each managerial feature set to improving the prediction accuracy, and we found the reliability and operation environment are valuable feature sets in a significant way, so they should be collected, managed more carefully, and included for better prediction of spare parts demand of military aircraft.

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Using social network and semantic analysis to analyze online travel forums and forecast tourism demand

Cited by 82 publications

References 85 publications

The Past, Present, and Future of Smart Tourism Destinations: A Bibliometric Analysis

The Past, Present, and Future of Smart Tourism Destinations: A Bibliometric Analysis

Forecasting of Short-Term Daily Tourist Flow Based on Seasonal Clustering Method and PSO-LSSVM

Forecasting Spare Parts Demand of Military Aircraft: Comparisons of Data Mining Techniques and Managerial Features from the Case of South Korea

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