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Predicting U.S. Presidential Election through mining social media data. (Twitter)

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dc.contributor.author Gore, Palash
dc.date.accessioned 2016-05-25T18:23:31Z
dc.date.available 2016-05-25T18:23:31Z
dc.date.issued 2016-04-01
dc.identifier.uri https://scholarworks.bridgeport.edu/xmlui/handle/123456789/1641
dc.description.abstract Data mining is a term that refers to extraction of knowledge or information hidden in large volumes of raw data. The purpose of this project is to predict the popularity of a candidate for US presidential election, 2016 form each state using social media for a given time interval [t1, t2], where t1 is the set of tweets observed between the timestamp t2. Until recently, political parties used information that limited pursing or reaching out to the masses which restricted the scope of a widespread campaign. The outcome of the project will help political parties make proper decision and target the right audience. This project is making use of twitter API which introduce simple concepts to analyze data. It will emphasize on techniques and considerations for mining large amount of data that is posted on twitter in real time. en_US
dc.language.iso en_US en_US
dc.subject Data analysis en_US
dc.subject Data mining en_US
dc.subject Presidential election en_US
dc.subject Social media en_US
dc.title Predicting U.S. Presidential Election through mining social media data. (Twitter) en_US
dc.type Presentation en_US
dc.institute.department School of Engineering en_US
dc.institute.name University of Bridgeport en_US
dc.event.location Bridgeport, CT en_US
dc.event.name Faculty Research Day en_US

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