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Data Mining IEEE 2015 Projects
Web : www.kasanpro.com Email : sales@kasanpro.com
List Link : http://kasanpro.com/projects-list/data-mining-ieee-2015-projects
Title :Predicting iPhone Sales from iPhone Tweets
Language : ASP.NET with C#
Project Link : http://kasanpro.com/p/asp-net-with-c-sharp/predicting-iphone-sales-iphone-tweets
Abstract : Recent research in the field of computational social science have shown how data resulting from the
widespread adoption and use of social media channels such as twitter can be used to predict outcomes such as
movie revenues, election winners, localized moods, and epidemic outbreaks. Underlying assumptions for this
research stream on predictive analytics are that social media actions such as tweeting, liking, commenting and rating
are proxies for user/consumer's attention to a particular object/product and that the shared digital artefact that is
persistent can create social influence. In this paper, we demonstrate how social media data from twitter can be used
to predict the sales of iPhones. Based on a conceptual model of social data consisting of social graph (actors, actions,
activities, and artefacts) and social text (topics, keywords, pronouns, and sentiments), we develop and evaluate a
linear regression model that transforms iPhone tweets into a prediction of the quarterly iPhone sales with an average
error close to the established prediction models from investment banks. This strong correlation between iPhone
tweets and iPhone sales becomes marginally stronger after incorporating sentiments of tweets. We discuss the
findings and conclude with implications for predictive analytics with big social data.

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Data Mining IEEE 2015 Projects

  • 1. Data Mining IEEE 2015 Projects Web : www.kasanpro.com Email : sales@kasanpro.com List Link : http://kasanpro.com/projects-list/data-mining-ieee-2015-projects Title :Predicting iPhone Sales from iPhone Tweets Language : ASP.NET with C# Project Link : http://kasanpro.com/p/asp-net-with-c-sharp/predicting-iphone-sales-iphone-tweets Abstract : Recent research in the field of computational social science have shown how data resulting from the widespread adoption and use of social media channels such as twitter can be used to predict outcomes such as movie revenues, election winners, localized moods, and epidemic outbreaks. Underlying assumptions for this research stream on predictive analytics are that social media actions such as tweeting, liking, commenting and rating are proxies for user/consumer's attention to a particular object/product and that the shared digital artefact that is persistent can create social influence. In this paper, we demonstrate how social media data from twitter can be used to predict the sales of iPhones. Based on a conceptual model of social data consisting of social graph (actors, actions, activities, and artefacts) and social text (topics, keywords, pronouns, and sentiments), we develop and evaluate a linear regression model that transforms iPhone tweets into a prediction of the quarterly iPhone sales with an average error close to the established prediction models from investment banks. This strong correlation between iPhone tweets and iPhone sales becomes marginally stronger after incorporating sentiments of tweets. We discuss the findings and conclude with implications for predictive analytics with big social data.