Cardiff University | Prifysgol Caerdydd ORCA
Online Research @ Cardiff 
WelshClear Cookie - decide language by browser settings

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, 23-25 November 2016.
Item availability restricted.

[img] PDF - Accepted Post-Print Version
Restricted to Repository staff only

Download (299kB)
Official URL: http://www.iscmi.us

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
Last Modified: 16 Jun 2017 08:52
URI: http://orca.cf.ac.uk/id/eprint/101409

Actions (repository staff only)

Edit Item Edit Item

Full Text Downloads from ORCA for this publication

Top Downloads of this item by Country

Monthly Full Text Downloads of this item

More statistics for this item...