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1.
    
This study aims to use computational linguistics, visual analytics, and deep learning techniques to analyze hotel reviews and responses collected on TripAdvisor and to identify response strategies. To this end, we collected and analyzed 113,685 hotel reviews and responses and their semantic and syntactic relations. We are among the first to use visual analytics and deep learning-based natural language processing to empirically identify managerial responses. The empirical results indicate that our proposed multi-feature fusion, convolutional neural network model can make different types of data complement each other, thereby outperforming the comparisons. The visualization results can also be used to improve the performance of the proposed model and provide insights into response strategies, which further shows the theoretical and technical contributions of this study.  相似文献   

2.
    
Despite the increased recognition of the importance of images as a rich data source, the full potential of images to advance tourism and hospitality knowledge, both conceptually and theoretically, has yet to be fully tapped. This study provides a critical review of image analytics, examining its broad implications for tourism and hospitality research. This paper offers the progress of image definitions, features and related theories, as well as presents a methodological framework for conducting image-related studies, complementing the dominant textual analysis used in tourism and hospitality research. The paper makes contributions to the tourism methodological literature by developing a point of reference for the application of image analytics in tourism and hospitality studies.  相似文献   

3.
    
Online consumer reviews have been studied for various research problems in hospitality and tourism. However, existing studies using review data tend to rely on a single data source and data quality is largely anecdotal. This greatly limits the generalizability and contribution of social media analytics research. Through text analytics this study comparatively examines three major online review platforms, namely TripAdvisor, Expedia, and Yelp, in terms of information quality related to online reviews about the entire hotel population in Manhattan, New York City. The findings show that there are huge discrepancies in the representation of the hotel industry on these platforms. Particularly, online reviews vary considerably in terms of their linguistic characteristics, semantic features, sentiment, rating, usefulness as well as the relationships between these features. This study offers a basis for understanding the methodological challenges and identifies several research directions for social media analytics in hospitality and tourism.  相似文献   

4.
Online consumer reviews (OCRs) are valuable to consumers and sellers. Online price promotion is commonly used by local merchants to increase sales. However, knowledge of the differences in OCRs between consumers who received a discount and regular consumers is limited. This study investigates the effects of price discounts on restaurant OCRs by comparing the review rating and open-ended contents of OCRs from consumers who received a discount and regular consumers. The results show that the review rating is higher from consumers who received a discount, whereas the word count, image count, and diversity of review contents are higher from regular consumers. Regular consumers are more likely to mention product quality, environmental quality, service quality, geographic location, purchasing process, recommendation expression, and loyalty expression in OCRs, and there is no significant difference in the dimensions of price, cognitive attitude, and emotional attitude between the two groups.  相似文献   

5.
The 7 Ps model is a very useful tool in helping service firms solve managerial issues in marketing. Guided by the 7 Ps marketing mix framework, a big-data, supervised machine learning analysis was performed with 1,148,062 English reviews of 37,092 Airbnb listings in San Francisco and New York City. The results disclose similar patterns in both markets, where travelers shared their experience about Service Product and Physical Evidence most often; Price and Promotion were the least mentioned elements. Furthermore, through a series of comparisons of Airbnb’s 7 Ps marketing mix among the listings managed by different types of hosts, multi-unit and single-unit hosts seem to offer similar services with a small observable difference; whereas superhosts and the ordinary hosts deliver different services. This study makes valuable methodological contributions and provides practical marketing insights for hoteliers and the hosts and webmasters on home-sharing websites. Policymakers should pay special attention to multi-unit hosts.  相似文献   

6.
    
The inclusion of a photo in users’ profile provides information about them and shows a higher sense of self-expression and potential engagement. On peer-to-peer rental platforms, profile images may be useful for hosts and guests to infer individual characteristics and expectations. We try to fill a gap in the literature by inferring guests’ posting behavior through their profile image. Using Airbnb data and deep learning techniques, our empirical analysis reveals that guests who upload profile images—especially profile images displaying happy emotions—are more involved in posting long reviews. As theoretical implications, these results add knowledge to the application of the Five Factor Model of Personality, deep learning, image recognition, and emotion recognition in hospitality. As managerial implications, the prediction of posting behavior through the mining of visual information can be a relevant tool in the age of big data.  相似文献   

7.
    
Online reviews remain important during the COVID-19 pandemic as they help customers make safe dining decisions. To help restaurants better understand customers’ needs and sustain their business under current circumstance, this study extracts restaurant features that are cared for by customers in current circumstance. This study also introduces deep learning methods to examine customers’ opinions about restaurant features and to detect reviews with mismatched ratings. By analyzing 112,412 restaurant reviews posted during January-June 2020 on Yelp.com, four frequently mentioned restaurant features (e.g., service, food, place, and experience) along with their associated sentiment scores were identified. Findings also show that deep learning algorithms (i.e., Bidirectional LSTM and Simple Embedding + Average Pooling) outperform traditional machine learning algorithms in sentiment classification and review rating prediction. This study strengthens the extant literature by empirically analyzing restaurant reviews posted during the COVID-19 pandemic and discovering suitable deep learning algorithms for different text mining tasks.  相似文献   

8.
    
Previous studies have shown that online data, such as search engine queries, is a new source of data that can be used to forecast tourism demand. In this study, we propose a forecasting framework that uses machine learning and internet search indexes to forecast tourist arrivals for popular destinations in China and compared its forecasting performance to the search results generated by Google and Baidu, respectively. This study verifies the Granger causality and co-integration relationship between internet search index and tourist arrivals of Beijing. Our experimental results suggest that compared with benchmark models, the proposed kernel extreme learning machine (KELM) models, which integrate tourist volume series with Baidu Index and Google Index, can improve the forecasting performance significantly in terms of both forecasting accuracy and robustness analysis.  相似文献   

9.
    
Photographs with a human element are powerful in influencing viewers' perceptions and decision-making processes. However, rare quantitative evidence was detected about the best human presenting percentage and how it affects the intention. In this study, we decoded how human elements (presence or absence, low or high proportion) affect viewers' perceptions and intentions in nature/culture-based photographs. Innovatively, three deep learning models and two experiments were integrated. The results indicate that (1) in general, maintaining the proportion of human elements at less than 1% leads to the best positive perception, and (2) the viewers demonstrate different perceptions and intentions in viewing nature-based and culture-based photographs with the human element. Theoretically, we bring a new perspective and approach to understanding the marketing value of human elements in tourist-generated photographs. Practically, we provide specific and different clues in choosing photographs to promote cultural and natural destinations regarding human elements.  相似文献   

10.
    
Increasing competition and adoption of revenue management practices in the hotel industry fuel the need for accurate forecasting to maximize profits and optimize operations. Considering the limitations of relevant research, this study focuses on the daily hotel demand with consideration of agglomeration effect, and proposes a novel deep learning-based model, namely, Deep Learning Model with Spatial and Temporal correlations. This model contributes to relevant research by introducing the agglomeration effect and integrating the attention mechanism and Bayesian optimization algorithm. Historical daily demand data of 210 hotels in Xiamen, China are used to verify the model performance. Results show that the proposed model is significantly better than the benchmarks. This study can help hotel managers improve revenue management through better matching potential demand to available capacity.  相似文献   

11.
    
This study considers the review reliability problem by identifying biased user-given ratings through rating prediction on the basis of the textual content. Deep learning approaches were introduced to investigate the textual review and validate the effect of rating prediction using a dataset collected from Yelp. The definition of “biased rating” was clarified and influenced the matching rules. The approach obtains high performance on a total of 1,000,000 reviews for prediction, with user-given ratings as the benchmark. Using the revealed biased ratings, unreliable reviews were detected by combining the results of several deep learning kernels. Findings shed light on understanding review quality by distinguishing biased ratings and unreliable reviews that may cause inconsistency and ambiguity to readers. Hence, theoretical and managerial areas for social media analytics are enriched on the basis of online review meta-data in hospitality and tourism.  相似文献   

12.
本文以旅游资源型小城镇安徽省灵璧县为例,以居民对旅游影响的感知、态度为基础,利用自组织神经网络方法,对居民旅游影响感知进行分类研究,并对分类结果进行深入剖析.通过分类研究,把握旅游地居民感知特征,为旅游地的可持续发展、旅游规划提供依据.同时作者将分类结果与前人对不同发展阶段旅游地的分类结果进行比较分析.验证了旅游地发展阶段理论,证实了神经网络方法在居民旅游影响感知分类研究中的适用性,促进了旅游学科与更多方法的融合.  相似文献   

13.
    
Identifying and presenting helpful reviews to customers can significantly affect their purchase decisions. Although review helpfulness has been extensively explored in tourism research, extant studies have not sufficiently emphasized the unique characteristics of tourism products and investigated review helpfulness perceptions from both geographic and social influence perspectives. In this study, drawing on social contagion theory, we developed a theoretical framework to examine the impact of social contagion, specifically geographic and social proximities, on perceived review helpfulness. Our empirical analyses of Yelp restaurant reviews indicated that geographic and social influences have varying impacts on review helpfulness perceptions. Additionally, social contagions significantly moderated the impacts of various review- and reviewer-related factors, and product characteristics further moderated the contagion effect on perceived review helpfulness. This study provides valuable theoretical and methodological contributions to research on review helpfulness, especially in tourism contexts, and lays out the practical implications for various stakeholders.  相似文献   

14.
    
ABSTRACT

This study introduces the concept of long short-term memory (LSTM) network to handle complex time series forecasting problems in the tourism industry. To validate the efficiency of the developed method, we used the daily tourist flow and consumer search data of Jiuzhaigou, a popular tourist spot in China, from 8 October 2013 to 7 August 2017 as the experimental dataset for empirical analysis. According to the 150-day forecasting results, LSTM shows the best statistical performance in the training and test sets compared with its counterparts.  相似文献   

15.
基于BP神经网络和ARIMA组合模型的中国入境游客量预测   总被引:11,自引:1,他引:11  
雷可为  陈瑛 《旅游学刊》2007,22(4):20-25
游客量的预测和分析是旅游规划与管理的基础性、关键性工作.目前,游客量预测主要采用基于传统研究方法或人工神经网络技术的单项预测方法.近年来的研究表明,组合预测方法比单项预测具有更高的预测精度.本文提出了一种基于BP神经网络和ARIMA组合模型的游客量预测新方法,对中国入境旅游人次数的变化趋势进行了综合分析与预测,预测结果表明这种方法相对于单一的预测方法具有更高的精度,该模型在旅游预测中的应用是可行、有效的.  相似文献   

16.
In the e-tourism era, online customer reviews with pictures, textual comments, etc. have become a significant information source affecting customers’ purchase intention and behavior. Big data analytics with text mining, semantic network analysis and factor analysis, and regression analysis were applied to help understand cruiser experience and its relationship to cruiser satisfaction in the Asian cruise market through online cruiser reviews. Research results confirmed the dimensions of cruise experience from previous studies, while more importantly, “Onshore attributes” was explored in this study using the insight of big data, which was never investigated as a facet of cruise experience previously.  相似文献   

17.
    
Cancellations are a key aspect of hotel revenue management because of their impact on room reservation systems. In fact, very little is known about the reasons that lead customers to cancel, or how it can be avoided. The aim of this paper is to propose a means of enabling the forecasting of hotel booking cancellations using only 13 independent variables, a reduced number in comparison with related research in the area, which in addition coincide with those that are most often requested by customers when they place a reservation. For this matter, machine-learning techniques, among other artificial neural networks optimised with genetic algorithms were applied achieving a cancellation rate of up to 98%. The proposed methodology allows us not only to know about cancellation rates, but also to identify which customer is likely to cancel. This approach would mean organisations could strengthen their action protocols regarding tourist arrivals.  相似文献   

18.
    
This paper proposes a methodology for conducting importance-performance analysis (IPA) through online reviews. The methodology is composed of three stages: (1) mining useful information from online reviews, (2) estimating each attribute's performance and importance, and (3) constructing IPA plot, where the latent dirichlet allocation (LDA), the improved one-vs-one strategy based support vector machine (IOVO-SVM) and the ensemble neural network based model (ENNM) are respectively used. A case study on two five-star hotels is given, and the results obtained by the proposed methodology through online reviews are compared with those obtained by the existing methods through questionnaires (or online ratings). The results indicate that the proposed methodology can obtain effective analysis results with lower cost and shorter time since online reviews are publicly available and easily collected. The proposed methodology can give managers or market analysts one more choice for conducting IPA or serve as a preparing process of large-scale survey.  相似文献   

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