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Social Media Big Data (SMBD)

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Social Media Big Data (SMBD)

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dc.contributor.advisor Neagu, Daniel es_ES
dc.contributor.advisor Peng, Yonghong es_ES
dc.contributor.advisor Álvarez Sánchez, Diego es_ES
dc.contributor.author García Cantos, José Francisco es_ES
dc.date.accessioned 2014-11-04T10:09:39Z
dc.date.available 2014-11-04T10:09:39Z
dc.date.created 2014-07-22
dc.date.issued 2014-11-04T10:09:39Z
dc.identifier.uri http://hdl.handle.net/10251/43835
dc.description.abstract This project is to build a system that allows us, using social media big data such as Twitter, to know how people reacts and behave when some events or news occur. Big Data is a great information source stored on Internet and that could be analyzed by software, this could mean an advantage and a special relation with their consumers, using this information as a feedback. The volume of data is one of the biggest drawbacks the project is working on showing the best possible results in a short period of time. The computer software developed in this project includes the following functions:  Providing access to the information on Internet, restricting it, to social networks area.  Providing treatment of the information to allow dividing it into different categories such as, who, where and what someone wrote.  Providing data visualization to show it using simple tools like tables or different graphics. es_ES
dc.format.extent 94 es_ES
dc.language Inglés es_ES
dc.publisher Universitat Politècnica de València es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject.classification COMUNICACION AUDIOVISUAL Y PUBLICIDAD es_ES
dc.subject.other Grado en Ingeniería Informática-Grau en Enginyeria Informàtica es_ES
dc.title Social Media Big Data (SMBD) es_ES
dc.type Proyecto/Trabajo fin de carrera/grado es_ES
dc.rights.accessRights Cerrado es_ES
dc.contributor.affiliation Universitat Politècnica de València. Escola Tècnica Superior d'Enginyeria Informàtica es_ES
dc.description.bibliographicCitation García Cantos, JF. (2014). Social Media Big Data (SMBD). http://hdl.handle.net/10251/43835. es_ES
dc.description.accrualMethod Archivo delegado es_ES


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