El texto completo de este trabajo no está disponible en el Repositorio Académico UPC por restricciones de la casa editorial donde ha sido publicado. / In recent years, the increased use of digital tools within the Peruvian tourism industry has created a corresponding increase in revenues. However, both factors have caused increased competition in the sector that in turn puts pressure on small and medium enterprises’ (SME) revenues and profitability. This study aims to apply neural network based sentiment analysis on social networks to generate a new information search channel that provides a global understanding of user trends and preferences in the tourism sector. A working data-analysis framework will be developed and integrated with tools from the cloud to allow a visual assessment of high probability outcomes based on historical data, to help SMEs estimate the number of tourists arriving and places they want to visit, so that they can generate desirable travel packages in advance, reduce logistics costs, increase sales, and ultimately improve both quality and precision of customer service.
| Identifer | oai:union.ndltd.org:PERUUPC/oai:repositorioacademico.upc.edu.pe:10757/656348 |
| Date | 01 January 2020 |
| Creators | Murga, Javier, Zapata, Gianpierre, Chavez, Heyul, Raymundo, Carlos, Rivera, Luis, Domínguez, Francisco, Moguerza, Javier M., Álvarez, José María |
| Publisher | Springer Science and Business Media Deutschland GmbH |
| Source Sets | Universidad Peruana de Ciencias Aplicadas (UPC) |
| Language | English |
| Detected Language | English |
| Type | info:eu-repo/semantics/article |
| Format | application/html |
| Source | Universidad Peruana de Ciencias Aplicadas (UPC), Repositorio Académico - UPC, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12390 LNCS, 199, 219 |
| Rights | info:eu-repo/semantics/embargoedAccess |
| Relation | https://link.springer.com/chapter/10.1007/978-3-662-62308-4_8 |
Page generated in 0.0025 seconds