Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/137944
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dc.contributor.authorYak, Kenneth Yong Sengen_US
dc.date.accessioned2020-04-20T05:28:53Z-
dc.date.available2020-04-20T05:28:53Z-
dc.date.issued2020-
dc.identifier.urihttps://hdl.handle.net/10356/137944-
dc.description.abstractBusinesses are slowly shifting to social networking channels to market their products such as Twitter rather than traditional marketing and advertising. Brands create content to disseminate information to consumers. They use various methods to attract a large number of audiences such as likes and comments to gain popularity. These insights can prove useful to smaller start-up companies which can help them to generate new marketing ideas as well as advertisements. This project aims to develop a web platform to generate a popularity distribution among different data retrieved from Twitter. This will allow the smaller organization to find out various approaches of larger companies of their high level of interaction and audiences, and the difference in their interactivity level compared to those smaller companies.en_US
dc.language.isoenen_US
dc.publisherNanyang Technological Universityen_US
dc.relationSCSE19-0331en_US
dc.subjectEngineering::Computer science and engineering::Computing methodologies::Document and text processingen_US
dc.titleMining social media dataen_US
dc.typeFinal Year Project (FYP)en_US
dc.contributor.supervisorKe Yiping, Kellyen_US
dc.contributor.schoolSchool of Computer Science and Engineeringen_US
dc.description.degreeBachelor of Engineering (Computer Science)en_US
dc.contributor.researchCentre for Computational Intelligenceen_US
dc.contributor.supervisoremailypke@ntu.edu.sgen_US
item.grantfulltextrestricted-
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Appears in Collections:SCSE Student Reports (FYP/IA/PA/PI)
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