Using deep learning and visual analytics to explore hotel reviews and responses

Chang, Yung Chun; Ku, Chih Hao; Chen, Chien Hung

doi:10.1016/j.tourman.2020.104129

Cited by 78 publications

(49 citation statements)

References 138 publications

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“…Cheng et al [46] trained a deep CNN model with AirBnB reviews to predict potential guests' trust perception over a hospitality establishment. Chang et al [47] analyzed hotel reviews and responses by using visual analytics, computational linguistics and Deep Learning to detect proactive hotel responses, using a CNN-based multi-feature fusion system. Lastly, Luo et al [48] applied Deep Learning to model the experiences of Chinese economy hotel clients, using a bidirectional long short-term memory model combined with a conditional random field model.…”

Section: Sentiment Analysis and Satisfaction Degreementioning

confidence: 99%

Computational Intelligence in the hospitality industry: A systematic literature review and a prospect of challenges

Guerra-Montenegro

Sánchez-Medina

Laña

et al. 2021

Applied Soft Computing

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This research work presents a detailed survey about Computational Intelligence (CI) applied to various Hotel and Travel Industry areas. Currently, the hospitality industry's interest in data science is growing exponentially because of their expected margin of profit growth. In order to provide precise state of the art content, this survey analyzes more than 160 research works from which a detailed categorization and taxonomy have been produced. We have studied the different approaches on the various forecasting methods and subareas where CI is currently being used. This research work also shows an actual distribution of these research efforts in order to enhance the understanding of the reader about this topic and to highlight unexploited research niches. A set of guidelines and recommendations for future research areas and promising applications are also presented in a final section.

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Section: Sentiment Analysis and Satisfaction Degreementioning

confidence: 99%

Computational Intelligence in the hospitality industry: A systematic literature review and a prospect of challenges

Guerra-Montenegro

Sánchez-Medina

Laña

et al. 2021

Applied Soft Computing

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“…(Zhang, 2019) Machine learning approaches outperform traditional models (Karaoglan, Temizkan, & Findik, 2019) Conducted sentimental analysis of hotels reviews (Al Shehhi & Karathanasopoulos, 2020) Machine learning performance is far better than the seasonal autoregressive integrated moving average (SARIMA) model. (Chang, Ku, & Chen, 2020) Analyzed hotel reviews (Sánchez-Medina & Eleazar, 2020) Predicted hotel booking and cancellation using ANN 86 | P a g e https://jaauth.journals.ekb.eg/…”

Section: P a G Ementioning

confidence: 99%

Application of Machine Learning in the Hotel Industry: A Critical Review

Alotaibi

2020

Journal of Association of Arab Universities for Tourism and Hos

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Study purpose-The hotel industry like any other industry is witnessing a change due to information and communication technology. However, this change is quite slow. Many researchers in recent time have garnered interest in exploring and implementing the new technologies of artificial intelligence and machine learning in the hotel industry. Therefore, the purpose of this study is to give insights on the role of ML and its integrated technologies in the hotel industry. Design/Methodology/Approach-The study has critically reviewed articles published from 2010 to 2020. To achieve the research objective, the study seeks to answer three main research questions related to the existing literature; RQ1: Where does the hotel industry implement machine learning? RQ2: What are the machine learning techniques used in the hotel industry? RQ3: Which countries are using machine learning in the hotel industry? Findings-The study found that machine learning is helpful in demand forecasting, price forecasting, booking cancellation prediction, financial efficiency, and work efficiency. The machine learning algorithms outperform in the forecast accuracy against the statistical models. The countries at the forefront in machine learning technologies are China and USA. The other countries should take the cue from them and implement machine learning in their hotels Originality of the research-This research conducts exploratory analysis to identify the extent of scientific community knowledge and awareness on machine learning in the hotel industry. To the best of the authors' knowledge, no prior researcher has conducted a similar study specifically in the hotel industry.

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“…FLANN is able to help mortgage lenders to decide whether to approve or reject the application. Chang et al (2020) compare 113 685 reviews from the web TripAdvisor. Using the convolutional model of NN and its deep learning, they monitor the complementarity of various types of data.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Fuzzy Logic and Artificial Intelligence Tools in the Context of Tourism Analysis

Hašková¹,

Valuation²,

Budejovice³

et al. 2020

International Thematic Monograph: Modern Management Tools and Economy of Tourism Sector in Present Era

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Qualitative and quantitative approaches to multicriteria evaluation and managerial decision- making often ignore the specifics of the role of the human factor. This article summarizes management methods that reflect not only numerical inputs but also data of a qualitative nature while considering their applicability in the tourism sector. Some of them can be assorted within the classes of Artificial intelligence. The focus is on the fuzzy approach at the theoretical and application level. The fuzzy approach is used to evaluate the degree of country travel and tourism competitiveness of selected European and Asian countries based on subjective rankings from the viewpoint of travelling persons. The results indicate that among countries under review, China is ranked as a highly competitive country in travel & tourism. Conditional competitive countries in terms of travel & tourism are the Czech Republic, Pakistan, Russia, and Turkey.

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Using deep learning and visual analytics to explore hotel reviews and responses

Cited by 78 publications

References 138 publications

Computational Intelligence in the hospitality industry: A systematic literature review and a prospect of challenges

Computational Intelligence in the hospitality industry: A systematic literature review and a prospect of challenges

Application of Machine Learning in the Hotel Industry: A Critical Review

Fuzzy Logic and Artificial Intelligence Tools in the Context of Tourism Analysis

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