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Online reviews are becoming increasingly abundant, which makes them sometimes overwhelming for the users. To mitigate the problem of information overload, online retailers often proceed to display them according to their helpfulness to other users. In recent years, research has been aimed at finding efficient ways to automatically predict review helpfulness. This paper offers insight on both the most appropriate algorithm for the task of predicting review helpfulness in the specific context of class imbalance and high overlap of class features, and on the pre-processing techniques which can improve classifier performance in that context. To do so, it considers three classification algorithms: random forest, multinomial naive Bayes and linear support vector machine that uses stochastic gradient descent for learning. It shows that : (1) none of the considered algorithm exhibit satisfying performance when facing imbalanced datasets and similar class features; (2) the use of linguistic pre-processing techniques results in marginal or no improvement; (3) the use of frequency-based pre- processing yields moderate improvement; (4) re-sampling techniques are highly efficient, especially Synthetic Minority Over-sampling TEchnique (SMOTE); (5) Overall, random forest combined with SMOTE shows the best performance in terms of precision, recall and F1-score.
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This book collected studies focused on the management of tourist destinations. Destinations are complex and adaptive systems, where the different elements that make them up have to be oriented towards achieving a common objective that improves the competitiveness of the destination. Five main lines of research on tourist destinations can be established: 1) the management, planning, and marketing of destinations, with special attention to the tourism supply chain, communication, and integral management; 2) the sustainability of resources and capabilities; 3) the renewal of destinations in order to update their offer and main resources to maintain competitiveness; 4) online reputation and communication through social media in order to create and enhance a strong brand image and customer loyalty; and 5) the application of new technologies in order to develop smart destinations. The book is made up of five research studies that focus on analyzing the transition towards a more circular tourist activity in hotels, image as a competitive factor of destinations, the value of cultural creativity, the coherence of online reputation, and the relationship between hotel prices and online reputation in different tourist destinations.
Upper Silesian Conurbation --- post-industrial cities development --- abstract and figurative clues --- rating of online reputation --- price --- hotel sector’s competitiveness --- regional image --- innovation --- lodging --- online reputation --- service quality --- online customer review --- tourism destination --- customer online review --- community manager --- sustainable tourism --- added value --- cultural and creative community --- social media content exploration --- circular economy --- negative stereotypes --- destination image
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