A K-means Group Division and LSTM Based Method for Hotel Demand Forecasting

The accuracy of hotel demand forecasting is affected by factors such as the completeness of historical data and the maturity of models. Most of the existing methods are based on rich data, without considering that single hotels may only obtain sparse data. Therefore, a K-means group division and Long Short-Term Memory (LSTM) based method is proposed in this paper. Guest types are introduced into the forecasting to provide reference for hotel\u27s further decision-making. Using an example of 1493 hotels in Europe, we divide hotel groups and forecast the flow of leisure and business guests. The experimental results show that, compared with the benchmark models, LSTM can improve the forecasting performance of hotel group; compared with single hotels, the forecasting of hotel groups can effectively avoid inaccuracy caused by sparse data. The results can provide necessary reference for hospitality to make decisions based on guest types

A Latent Dirichlet Allocation Technique for Opinion Mining of Online Reviews of Global Chain Hotels

The hospitality industry has faced unprecedented challenges with the outbreak of Covid-19, which has changed customers' expectations. Therefore, it is essential to identify customers' new perceptions and expectations that lead to positive and negative opinions towards the service providers. Accordingly, this study aims to perform topic modeling and sentiment analysis on 94,200 online reviews of five global chain hotels in South Asia. Topic modeling as a text mining, unsupervised machine learning technique can decipher topics from a corpus such as online reviews, online reports, news covers, etc. In this study, the data is extracted from Trip Advisor through web scraping. Topic modeling is performed using the Latent Dirichlet Approach (LDA) on the extracted data set to analyze the key topics mentioned by the customers in the online reviews. The analysis depicted that cleanliness, food, staff, and service were the main concerns of the hotel guests. Furthermore, the findings represented that the main issues impacting the hotel guests were service delays. However, food and services were the keywords with the maximum word count as depicted by topic modeling

Machine Learning for Forecasting Future Reservations’ Ratings : Radisson Blu Seaside in Helsinki

In the current age of internet and big data, it is imperative for hotels to enhance their online reputation to remain competitive and profitable. This research presents a new perspective on how hotels can maintain and improve their online reputation through the use of machine learning techniques to predict the ratings of reservations. The approach involves analysing data that customers provide when booking a room. Additionally, the study explores how insights gleaned from online textual reviews can be used by hotel managers to address negative ratings. The study's primary objective is to assess the effectiveness of machine learning in predicting negative instances, a critical factor in managing online reputation. The best performing models achieved a 60% accuracy in classifying negative instances. However, increasing the number of predicted true negative instances also increased the number of false negative instances. This result was primarily due to the unpredictability of customer behaviour, making it difficult to accurately predict ratings. Despite not achieving the desired result, this study presents a novel direction for future research and provides suggestions for future research ideas. By utilizing machine learning algorithms to analyse customer data, hotels can better understand their customer's preferences, allowing them to improve their online reputation and ultimately improve their bottom line

ChatGPT and Persuasive Technologies for the Management and Delivery of Personalized Recommendations in Hotel Hospitality

Recommender systems have become indispensable tools in the hotel hospitality industry, enabling personalized and tailored experiences for guests. Recent advancements in large language models (LLMs), such as ChatGPT, and persuasive technologies, have opened new avenues for enhancing the effectiveness of those systems. This paper explores the potential of integrating ChatGPT and persuasive technologies for automating and improving hotel hospitality recommender systems. First, we delve into the capabilities of ChatGPT, which can understand and generate human-like text, enabling more accurate and context-aware recommendations. We discuss the integration of ChatGPT into recommender systems, highlighting the ability to analyze user preferences, extract valuable insights from online reviews, and generate personalized recommendations based on guest profiles. Second, we investigate the role of persuasive technology in influencing user behavior and enhancing the persuasive impact of hotel recommendations. By incorporating persuasive techniques, such as social proof, scarcity and personalization, recommender systems can effectively influence user decision-making and encourage desired actions, such as booking a specific hotel or upgrading their room. To investigate the efficacy of ChatGPT and persuasive technologies, we present a pilot experi-ment with a case study involving a hotel recommender system. We aim to study the impact of integrating ChatGPT and persua-sive techniques on user engagement, satisfaction, and conversion rates. The preliminary results demonstrate the potential of these technologies in enhancing the overall guest experience and business performance. Overall, this paper contributes to the field of hotel hospitality by exploring the synergistic relationship between LLMs and persuasive technology in recommender systems, ultimately influencing guest satisfaction and hotel revenue.Comment: 17 pages, 12 figure

Using sentiment analysis in tourism research: A systematic, bibliometric, and integrative review

Purpose: Sentiment analysis is built from the information provided through text (reviews) to help understand the social sentiment toward their brand, product, or service. The main purpose of this paper is to draw an overview of the topics and the use of the sentiment analysis approach in tourism research. Methods: The study is a bibliometric analysis (VOSviewer), with a systematic and integrative review. The search occurred in March 2021 (Scopus) applying the search terms "sentiment analysis" and "tourism" in the title, abstract, or keywords, resulting in a final sample of 111 papers. Results: This analysis pointed out that China (35) and the United States (24) are the leading countries studying sentiment analysis with tourism. The first paper using sentiment analysis was published in 2012; there is a growing interest in this topic, presenting qualitative and quantitative approaches. The main results present four clusters to understand this subject. Cluster 1 discusses sentiment analysis and its application in tourism research, searching how online reviews can impact decision-making. Cluster 2 examines the resources used to make sentiment analysis, such as social media. Cluster 3 argues about methodological approaches in sentiment analysis and tourism, such as deep learning and sentiment classification, to understand the user-generated content. Cluster 4 highlights questions relating to the internet and tourism. Implications: The use of sentiment analysis in tourism research shows that government and entrepreneurship can draw and enhance communication strategies, reduce cost, and time, and mainly contribute to the decision-making process and understand consumer behavior

A Novel K-Means Clustered Support Vector Machine Technique for Prediction of Consumer Decision-Making Behaviour

A greater number of consumers are using social networks to express their feedback about the level of service provided by hotels. Online reviews from patrons can be used as a forum to enhance the level of service of hotels. Customer reviews are indeed a reliable and dependable source that aid diners in determining the quality of their cuisine. It is critical to develop techniques for evaluating client feedback on hotel services. In order to accurately anticipate the consumers' decision-making behaviors based on hotel internet evaluations, this study proposes a novel K-Means Clustered Support Vector Machine (KMC+SVM) technique. Principal Component Analysis (PCA) is employed to determine the characteristics from the preprocessed data while the Min-Max normalization approach is used to standardize the raw data. The performance of the suggested technique is then evaluated and contrasted with a few other methods that are currently in use in terms of accuracy, sensitivity, RMSE, and MAE. The findings demonstrated that segmenting customers based on their online evaluations can accurately predict their choices and assist hotel management in establishing priorities for service quality enhancements

How to monitor and generate intelligence for a DMO from online reviews

Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Marketing IntelligenceSocial media and customer review websites have changed the way the tourism sector is managed. Social media has become a new source of information, due to the large amount of UGC / e-Wom generated by consumers An information that is "available" but at the same time noisy and of great volume, which makes it difficult to access and analyze. This study investigates and verifies the possibility of using data present in content reviews of a Content Web Site Review - TripAdivsor - to generate actionable information for a Destination Management Organization. With a focus on negative reviews, tourist attractions of Lisbon and using the “R code” and its packages, the study shows that with the correct technique chosen and the action of an intelligence analyst, data can be extracted and provide substrate for actions, strategy and intelligence generation – which is Social Media Intelligence. The findings prove that the flood of web 2.0 data can serve as a source of intelligence for the Destination Management Organization (DMO). By monitoring sites like TripAdvisor, a DMO can hear what tourists talk about attractions and thereby generate insights for intelligence and strategy actions. A DMO can even, analyzing this data, make your attractions more desirable, and even act in adverse situations, reducing risky situations

EXPLORING AIRPORT NAVIGATION CHALLENGES FACED BY AIRLINE TRAVELERS WITH HIDDEN DISABILITIES

In this paper, the Servicescape theory was employed as the conceptual framework to (a) investigate the live airport experiences of passengers with an HD and (b) understand the negative experiences of passengers within the airport\u27s physical environment. The authors collected the electronic word-of-mouth statements of 203 travelers from the TripAdvisor website. The data generated were analyzed using thematic analysis. Common themes found from the analysis included (1) human interaction, (2) services, and (3) terminal design. This paper offers insight into airport navigational challenges faced by travellers with an HD at various stages within the airport. These findings have practical implications for airport operators and decision-makers implementing a Hidden Disability Assistance Program. The results may help airport operators, including airlines, understand passenger-customer interaction issues. Findings may equally help airport operators, while addressing identified challenges, to offer appropriate support effectively and efficiently to travellers with an HD when transiting through airports

Unlocking service provider excellence : expanding the touchpoints, context, qualities framework

Customer reviews offer scope for better understanding the customer experience (CX), which may be leveraged to improve firms' CX performance. We extend the Touchpoints, Context, Qualities (TCQ) nomenclature by integrating it with the ARC value-creation elements and the multiple dimensions of CX. Our extended TCQ framework comprises nine building blocks to delineate dynamic what we term CX performance trajectories. We test our framework by collecting verbatim text-based reviews, and transforming them into two robust data sets (weekly, and monthly), which we examine using a dynamic Hidden Markov Model. We identify three levels of CX performance states and the migrations paths between them. We find that the building blocks coherently express mechanisms that are effective at the weekly and monthly levels for helping firms improve, and prevent deterioration of, CX performance. This research enriches the CX and TCQ literature. In particular, we derive actionable guidance for managers to facilitate the dynamic management of their firm’s CX performance