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Topic identification system to filter Twitter feeds

Altammami, Shatha Hamad and Rana, Omer Farooq 2016. Topic identification system to filter Twitter feeds. Presented at: 2017 4th International Conference on Soft Computing & Machine Intelligence, Dubai, UAE, 23-25 November 2016.
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Abstract

Twitter is a micro-blogging service where users publish messages of 140 characters. This simple feature makes Twitter the source for concise, instant and interesting information ranging from friends’ updates to breaking news. However, a problem emerge when a user follows many accounts while interested in a subset of its content, which leads to overwhelming tweets he is not interested in receiving. We propose a solution to this problem by filtering incoming tweets based on the user’s interests, which is accomplished through a classifier. The proposed classifier system categorizes tweets into generic classes like Entertainment, Health, Sport, News, Food, Technology and Health. This paper describes the creation and evaluation of the classifier until 89% accuracy obtained

Item Type: Conference or Workshop Item (Paper)
Date Type: Completion
Status: In Press
Schools: Computer Science & Informatics
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Date of First Compliant Deposit: 14 June 2017
Last Modified: 31 Jan 2018 13:27
URI: http://orca.cf.ac.uk/id/eprint/101409

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