Topic identification system to filter Twitter feeds

Altammami, Shatha Hamad and Rana, Omer Farooq ORCID: https://orcid.org/0000-0003-3597-2646 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. Proceedings of 3rd International Conference on Soft Computing & Machine Intelligence.

Preview

PDF - Accepted Post-Print Version Download (299kB) | Preview

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Status:	Unpublished
Schools:	Schools > Computer Science & Informatics
Related URLs:	Organisation
Date of First Compliant Deposit:	14 June 2017
Last Modified:	11 Sep 2025 01:00
URI:	https://orca.cardiff.ac.uk/id/eprint/101409

Actions (repository staff only)

Edit Item