TY - GEN
T1 - Predictive model based on sentiment analysis for peruvian smes in the sustainable tourist sector
AU - Zapata, Gianpierre
AU - Murga, Javier
AU - Raymundo, Carlos
AU - Alvarez, Jose
AU - Dominguez, Francisco
N1 - Publisher Copyright:
© 2017 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved.
PY - 2017
Y1 - 2017
N2 - 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.
AB - 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.
KW - Big data
KW - Cloud computing
KW - Sentiment analysis
KW - Tourism sector
KW - Travel management process
UR - https://www.scopus.com/pages/publications/85055491777
U2 - 10.5220/0006583302320240
DO - 10.5220/0006583302320240
M3 - Contribución a la conferencia
AN - SCOPUS:85055491777
SN - 9789897582738
T3 - IC3K 2017 - Proceedings of the 9th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management
SP - 232
EP - 240
BT - IC3K 2017 - Proceedings of the 9th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management
A2 - Liu, Kecheng
A2 - Salgado, Ana Carolina
A2 - Bernardino, Jorge
A2 - Filipe, Joaquim
A2 - Filipe, Joaquim
PB - SciTePress
T2 - 9th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, IC3K 2017
Y2 - 1 November 2017 through 3 November 2017
ER -