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Despite numerous studies that adopt text analysis in tourism and hospitality published in the past decade, a comprehensive review of this topic is still lacking. This study endeavors to bridge this gap by conducting a systematic review of text analysis research published in the top five journals in tourism and hospitality between 2013 and 2023. From a thematic analysis perspective, this paper provides an elaborate description of how text data can be utilized for prediction and understanding purposes. Specifically, the prediction scenarios encompass hotels, destinations, and restaurants, while the understanding aspect focuses on tourists, firms, destinations, and academic researches within this domain. From a methodological perspective, this paper offers an in-depth exploration of data sources, tools or methodologies, processing procedures, and commonly utilized metrics in text analysis. The paper concludes by proposing a summary framework for text analysis research while highlighting future research trends, opportunities, and challenges in this domain. [ABSTRACT FROM AUTHOR]

Understanding the asymmetric relationships between satisfaction factors and overall employee satisfaction is critical for airlines if they are to formulate effective strategies for improving job satisfaction. However, little research has been done on this topic. In this study penalty–reward contrast analysis is adopted to analyze 53,718 items of employee-generated data from 225 airlines. The results show that work/life balance, career opportunities, compensation & benefits and senior management are 'basic' factors, and culture & values is a 'performance' factor. Furthermore, the asymmetric impacts of these factors vary with employment status and geographic region. Additionally, the priorities attached to these satisfaction factors by employees of different employment status from different regions are analyzed using 3-dimensional asymmetric impact performance analysis. The obtained results are valuable for formulating strategies to improve employee satisfaction in the airline industry. [ABSTRACT FROM AUTHOR]

To forecast the tourism demand across a set of tourist attractions with spatial dependence, a new model is proposed, which has three stages: tourist attraction selection, base predictor generation, and base predictor combination. In stage 1, a method for selecting associated attractions based on multi-dimensional scaling is used to determine the strength of the spatial dependence between each pair of attractions. In stage 2, a hybrid base predictor based on LSTM networks and Autoregressive model is developed, where the LSTM networks are used to capture the spatial dependence among attractions, and the Autoregressive model is used capture the scale of tourist volume at each attraction. In stage 3, a strategy for combining these base predictors is proposed; it can alleviate the overfitting problem of LSTM and improve the stability of forecasts. Finally, the superiority of the model is verified through the data on tourist volumes at 77 attractions in Beijing. [ABSTRACT FROM AUTHOR]

Compared with coarse-grained forecasting, fine-grained tourism demand forecasting is a more challenging task, but research on this issue is very scarce. To address this issue, a decomposition ensemble deep learning model is proposed by integrating CEEMDAN, CNNs, LSTM networks, and AR models. The CEEMDAN can decompose complex tourism demand data into multiple components with simpler characteristics, thereby reducing the complexity of forecasting. The CNNs and LSTM networks can fully capture the locally recurring patterns and the long-term dependencies of the components obtained by CEEMDAN. The AR model can capture the scale of tourism demand data, which can overcome the problem that the output scale of the deep neural networks (i.e., CNNs and LSTM networks) is not sensitive to the scale of the inputs. The effectiveness of the proposed model is verified by comparing with five benchmark models using real-time data on tourist volumes at two attractions. [ABSTRACT FROM AUTHOR]

With the rapid advances in information technology, an increasing number of online reviews are posted daily on the Internet. Such reviews can serve as a promising data source to understand customer satisfaction. To this end, in this paper, we proposed a method for modelling customer satisfaction from online reviews. In the method, customer satisfaction dimensions (CSDs) are first extracted from online reviews based on latent dirichlet allocation (LDA). The sentiment orientations of the extracted CSDs are identified using a support vector machine (SVM). Then, considering the existence of complex relationships among different CSDs and the customer satisfaction, an ensemble neural network based model (ENNM) is proposed to measure the effects of customer sentiments toward different CSDs on customer satisfaction. On this basis, to identify the category of each CSD from the customer's perspective, an effect-based Kano model (EKM) is proposed. Finally, an empirical study, which consists of two parts (phones and cameras), is given to illustrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Accurate forecasting of daily tourism demand is a meaningful and challenging task, but studies on this issue are scarce. To address this issue, multisource time series data, relating to past tourist volumes, web search information, daily weather conditions, and the dates of public holidays, are selected as the forecasting variables. To fully capture the relationship between these forecasting variables and actual tourism demand automatically, an ensemble of long short-term memory (LSTM) networks is proposed with a correlation-based predictor selection (CPS) algorithm. The effectiveness of the proposed method is verified in daily tourism demand forecasting for the Huangshan Mountain Area, benchmarked against 11 forecasting methods. This study contributes to the literature by (1) introducing the use of big data in daily tourism demand forecasting, (2) proposing an ensemble of LSTM networks for daily tourism demand forecasting, and (3) providing an effective predictor selection algorithm in ensemble learning. [ABSTRACT FROM AUTHOR]

This study proposes a hybrid method for producing personalized travel recommendation that better meet travellers'individual needs and also improve their online booking experience. The proposed method integrates multi-attribute collaborative filtering with social network analysis within the framework of large-scale group decision-making. It includes four modules, i.e. identification of online opinion experts, construction of a social network, detection of user communities, and interactively produced of personalized travel recommendation. Specifically, the preliminary user filtering and k-means clustering approach are utilized to identify the online opinion experts for a specific travel recommendation issue. Then, social network construction and its community detection process are adopted to alleviate the sparsity problem. Finally, the travel alternatives are ranked to select recommendations, and this is done interactively with travellers to handle the cold start problem. With the proposed method, a better online booking experience can be achieved for travellers, as they are presented with a more appropriate set of recommended options and so can make better travel decisions. [ABSTRACT FROM AUTHOR]

This study explores how to select the optimal number of lagged inputs (NLIs) in international tourism demand forecasting. With international tourist arrivals at 10 European countries, the performances of eight machine learning models are evaluated using different NLIs. The results show that: (1) as NLIs increases, the error of most machine learning models first decreases rapidly and then tends to be stable (or fluctuates around a certain value) when NLIs reaches a certain cutoff point. The cutoff point is related to 12 and its multiples. This trend is not affected by the size of the test set; (2) for nonlinear and ensemble models, it is better to select one cycle of the data as the NLIs, while for linear models, multiple cycles are a better choice; (3) significantly different prediction results are obtained by different categories of models when the optimal NLIs are used. [ABSTRACT FROM AUTHOR]