In the today's market, there is a wide range of failed IT projects in specialized small and medium-sized companies because of poor control in the gap between the business and its vision. In other words, acquired goods are not being sold, a scenario which is very common in tourism retail companies. These companies buy a number of travel packages from big companies and due to lack of demand for these packages, they expire, becoming an expense, rather than an investment. To solve this problem, we propose to detect the problems that limit a company by re-engineering the processes, enabling the implementation of a business architecture based on sentimental analysis, allowing small and medium-sized tourism enterprises (SMEs) to make better decisions and analyze the information that most possess, without knowing how to exploit it. In addition, a case study was applied using a real company, comparing data
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.
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.
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