[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119748-en":3,"doc-seo-119748-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},119748,549758146520,"Patrick","https://ap-avatar.wpscdn.com/avatar/80002397d8c0411e94?_k=1775819394049821470",8,"Research & Report","Machine Learning for Forecasting Future Reservations’ Ratings - A Hotel Governance of Digitalization Master’s Thesis","This master’s thesis examines how hotels can strengthen online reputation by using machine learning to forecast the ratings of future reservations. The study analyzes booking-related customer data and links it to reservation outcomes, while also leveraging online textual reviews to guide managers in responding to negative ratings. The key aim is evaluating model effectiveness for identifying negative instances, with best models reaching about 60% accuracy in classifying negative cases despite trade-offs between true negative gains and false negative increases due to customer behavior uncertainty.","Tong Nie  \nMACHINE LEARNING FOR FORECASTING FUTURE RESERVATIONS’ RATINGS  \n⎯ Radisson Blu Seaside in Helsinki  \nMaster’s Thesis in Governance of  \nDigitalization  \nSupervisors: Prof. József Mezei Faculty of Social Sciences, Business and Economics  \nÅbo Akademi University  \nABSTRACT  \n\n| Subject: Governance of Digitalization |  |\n| --- | --- |\n| Writer: Tong Nie |  |\n| Title: Machine Learning for Forecasting Future Reservations’ Ratings ofa Hotel |  |\n| Supervisor: Prof. József Mezei |  |\n| Abstract:\u003Cbr>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.\u003Cbr>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.\u003Cbr>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. |  |\n| Keywords: big data, data analysis, predictive analysis, online reputation, hotel industry |  |\n| Date: 01.05.2023 | Number of pages: 102 |\n\nTABLE OF CONTENTS  \nTABLE OF CONTENTS................................................................................................ 1  \nLIST OF FIGURES ........................................................................................................4  \nLIST OF TABLES .......................................................................................................... 5  \n1 INTRODUCTION................................................................................................... 6  \n1.1 OBJECTIVE OF THE THESIS ................................................................................. 7  \n1.2 STRUCTURE OF THE THESIS ................................................................................ 8  \n2 BACKGROUND ................................................................................................... 10  \n2.1 BIG DATA......................................................................................................... 10  \n2.2 DATA ANALYTICS ............................................................................................ 11  \n2.3 THE HOTEL INDUSTRY IN THE ERA OF INTERNET AND BIG DATA ..................... 14  \n2.3.1 Hotels in the era of internet and Big Data .................................................. 15  \n2.3.2 Machine learning in the hotel industry ....................................................... 15  \n2.3.3 UGC in the era of internet and big data ..................................................... 17  \n2.4 THE NECESSITY OF FORECASTING RATINGS FOR FUTURE RESERVATIONS .......... 18  \n3 DATASET.............................................................................................................. 20  \n3.1 DATA SOURCE AND VARIABLES SELECTION ......................................................20  \n3.2 HOTEL SELECTION ............................................................................................","cbCainwZlquSOvLR","https://ap.wps.com/l/cbCainwZlquSOvLR","pdf",1675378,1,104,"English","en",105,"# Introduction\n## Objective of the thesis\n## Structure of the thesis\n# Background\n## Big data\n## Data analytics\n## The hotel industry in the era of internet and big data\n## The necessity of forecasting ratings for future reservations\n# Dataset\n## Data source and variables selection\n## Hotel selection\n# Methodology\n## Web scraping\n## EDA and data pre-processing for predicting ratings categories\n## Scikit-learn and algorithms for predicting ratings categories\n## Performance evaluation\n## Data analysis for gaining insights from text reviews","[{\"question\":\"What is the thesis trying to achieve?\",\"answer\":\"It aims to assess whether machine learning can forecast future reservation ratings, especially by predicting negative instances that matter for online reputation management.\"},{\"question\":\"What data sources and inputs are used in the study?\",\"answer\":\"The approach analyzes customer information provided during room booking and also uses insights from online textual reviews to understand and address negative ratings.\"},{\"question\":\"How well do the models perform in predicting negative ratings?\",\"answer\":\"The best-performing models reach about 60% accuracy for classifying negative instances, but improving true negative predictions also increases false negatives due to unpredictable customer behavior.\"}]","Machine Learning for Forecasting Future Reservations’ Ratings - 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