Analyzing geosocial media for decision making

Abstract

Web-based social networking has radically meddled in another methodology by enabling individuals to distribute their information alongside their areas to convey advantages to the network and the nation overall. The use of geo-social networks is growing significantly, and these networks are generating huge amounts of data. As a result, current geosocial media can be used in real-time as databases for countries, governments and other organizations. By breaking down the social conduct of a network in a specific zone, individuals can suggest a shop, lodgings, shabby markets, managing an account frameworks, promotions, and so forth dependent on their inclinations and impediments. Breaking down such a lot of information and settling on continuous choices is a testing errand. In this way, an effective framework for gathering information is proposed to settle on ongoing choices while distinguishing different occasions. In this paper we analyse the foursquare data which is a geosocial networking site and then detect the most populated venue or a place by analysing the tips given by users. The analysis is done on the New York city check-is and tips. The decision making is done based on the results to predict the most popular place or venue in a given location based upon the tips given by users.