Using Text Mining and Clustering Techniques on Tweets to Discover Trending Topics in Dubai

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dc.Location2015 T 58.5 H36
dc.SupervisorDr Sherief Abdullah
dc.contributor.authorHamadeh, Moutaz Wajih
dc.date.accessioned2017-05-04T06:03:11Z
dc.date.available2017-05-04T06:03:11Z
dc.date.issued2015-05
dc.description.abstractTwitter micro-blogging website is a hot and emerging area of research recently, users on Twitter post millions of tweets every day from all over the world, it is very difficult and challenging task to keep track of messages and filter them based on topical interest. This study uses text mining and clustering techniques to partition Dubai tweets into clusters of a same topical interest. Tweets corpus of Dubai were collected, they were presented through the bag of words model using TF-IDF weighting scheme, after that the output of text transformation was introduced to k-means clustering algorithm with cosine similarity measure. Findings indicate that heuristic evaluation techniques are not so helpful in this domain; also, the model has generated interesting clusters about trending topics and events in Dubai. In the end, an experiment was conducted over datasets collected from different timeframes to see what are the constant hot topics discussed in Twitter about Dubai. All of the findings in this report have been empirically analysed through real-word tweets dataset.en_US
dc.identifier.other120032
dc.identifier.urihttp://bspace.buid.ac.ae/handle/1234/994
dc.language.isoenen_US
dc.publisherThe British University in Dubai (BUiD)en_US
dc.subjecttext miningen_US
dc.subjectclustering techniquesen_US
dc.subjecttweetsen_US
dc.subjectDubaien_US
dc.subjectUnited Arab Emirates (UAE)en_US
dc.subjecttwitter micro-bloggingen_US
dc.titleUsing Text Mining and Clustering Techniques on Tweets to Discover Trending Topics in Dubaien_US
dc.typeDissertationen_US
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