Abstract
The development of social media has changed the way that travelers visit sightseeing spots. The social Internet of Things (IoT) allows products to automatically generate posts, share content and location information, and help build an online community of users based on their company’s products, so that marketing personnel can also get useful feedback and understand the user’s opinions. In tourism and hospitality industry, to enhance the revisit intention of passengers is an important issue for the purpose of increasing margin. In recent years, related researches had focused on the customers’ revisit behaviors and factors. However, few studies have investigated the related issues that travelers do not want to visit again. Failure to revisit may bring a great damage to the company’s revenue in the future. To avoid this situation, a text mining based approach will be proposed to identify non-revisit factors from online textual reviews in social media. Because it is impossible to determine whether a passenger has intention to revisit, this study proposed a text mining based approach which uses sentiment of text reviews to identify the passenger’s motivations (negative for revisit and non-negative for revisit). Then, feature selection methods, decision tree (DT), Least Absolute Shrinkage and Selection Operator (LASSO), and Support Vector Machines Recursive Feature Elimination (SVM-RFE) will be utilized to discover the important factors of non-revisit factor set. Back-propagation Neural Networks (BPN) and Support Vector Machines (SVM) will be employed to evaluate the effectiveness of selected feature sets. Finally, experimental results could be provided to travel service providers to improve service quality and effectively avoid non-revisit behaviors in the future.
Highlights
With the development of social media, the number of tourists has grown rapidly
STEP 2.2.1 DEFINE CANDIDATE FACTORS AND CLASS LABELS According to available literatures, we focus on revisit intentions issues in hotels, restaurants, tourist attractions and destinations, to identify the factors that might potentially affect visitors’ re-visit intentions, and build the feature vocabulary according to the definition of the factors
The effectiveness of selected feature sets will be evaluated by Support Vector Machines (SVM) and Back-propagation Neural Networks (BPN) classifiers
Summary
With the development of social media, the number of tourists has grown rapidly. The social Internet of Things (IoT) allows products/devices to automatically generate posts, share content and location information, and help build an online community of users based on their company’s products [126], [127], so that marketing personnel can get useful feedback and understand the user’s opinions. This study will use feature selection methods to extract the crucial factors that influence the visitor’s re-visit intention. This study will use SVM-RFE adoption as one of the feature selection methods to identify factors that affect passengers no longer visit.
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