Predictive model based on sentiment analysis for peruvian smes in the sustainable tourist sector

• Autores: Zapata, Gianpierre, Murga, Javier, Raymundo, Carlos, Alvarez, Jose, Dominguez, Francisco
• Formato: artículo
• Fecha de Publicación: 2017
• Institución: Universidad Peruana de Ciencias Aplicadas
• Repositorio: UPC-Institucional
• Lenguaje: inglés
• OAI Identifier: oai:repositorioacademico.upc.edu.pe:10757/656350
• Enlace del recurso: http://hdl.handle.net/10757/656350
• Nivel de acceso: acceso abierto
• Materia: Big data; Cloud computing; Sentiment analysis; Tourism sector; Travel management process

In the sustainable tourist sector today, there is a wide margin of loss in small and medium-sized enterprise (SMEs) because of a poor control in logistical expenses. In other words, acquired goods are note being sold, a scenario which is very common in tourism SMEs. These SMEs buy a number of travel packages to big companies and because of the lack of demand of said packages, they expire and they become an expense, not the investment it was meant to be. To solve this problem, we propose a Predictive model based on sentiment analysis of a social networks that will help the sales decision making. Once the data of the social network is analyzed, we also propose a prediction model of tourist destinations, using this information as data source it will be able to predict the tourist interest. In addition, a case study was applied to a real Peruvian tourist enterprise showing their data before and after using the proposed model in order to validate the feasibility of proposed model.