dc.contributor.authorMuhammad Fairul Akmaruddin Miswari
dc.date.accessioned2017-04-17T06:56:34Z
dc.date.available2017-04-17T06:56:34Z
dc.date.issued2017
dc.identifier.urihttp://hdl.handle.net/10356/70218
dc.description.abstractTwitter is a popular social networking site which allows users to get information such as news and trends. However, Twitter being a text-based social networking site, may not be suitable for certain pockets of people such as the elderly, people who often multi-task and the less literate. As such, Twittener is an alternative for users to interact with Twitter. It allows users to listen to tweets, instead of the traditional way of reading them. This project aims to enhance the Topic Processor component of Twittener and introduce a trending algorithm for the Trend Detector component. The Topic Processor component generates the topics from the tweets crawled from Twitter using the combination of Latent Dirichlet Allocation (LDA) and SumBasic algorithm. The Trend Detector aims to generate trending topics within a particular time frame. The purpose of this report is to document the development and implementation of the enhancement to the Twittener system.en_US
dc.format.extent44 p.en_US
dc.language.isoenen_US
dc.rightsNanyang Technological University
dc.subjectDRNTU::Engineering::Computer science and engineeringen_US
dc.titleTwittener : topic modellingen_US
dc.typeFinal Year Project (FYP)en_US
dc.contributor.supervisorOwen Noel Newton Fernandoen_US
dc.contributor.schoolSchool of Computer Science and Engineeringen_US
dc.description.degreeBachelor of Engineering (Computer Science)en_US


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