Text annotation using textual semantic similarity and term-frequency (Twitter)

Abaho, Michael, Gartner, Daniel ORCID: https://orcid.org/0000-0003-4361-8559, Cerutti, Federico ORCID: https://orcid.org/0000-0003-0755-0358 and Boulton, John 2018. Text annotation using textual semantic similarity and term-frequency (Twitter). Presented at: European Conference on Information Systems 2018, Portsmouth, UK, 23-28 June 2018. Research Papers. AIS Electronic Library (AISeL), p. 205.

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Abstract

Researchers on social-media understandably assert that the contributions social media has made on various sectors is massive. Business development managers today have directed a huge amount of effort in strategizing efficient collaboration with both customers and other organizations using social-media. Despite the visible impact social media has made, a lot of digitally shared information is yet to be revealed. Gradually twitter has become the main hub for many Information system researchers, because tweets can freely be accessible in real-time by any one. Motivated by earlier studies where IS researchers addressed big-data analysis and management by employing content analysis techniques, this paper proposes a novel approach to perform unsupervised classification of the tweets into different labels. It introduces a unique algorithm that uses semantic similarity between texts, Term-frequency and a determinant threshold to perform content analysis. The goal of this approach is to extract relevant features from a tweet thus reducing dimension and preparing training datasets that would be used to build classifiers.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Publication
Status:	Published
Schools:	Schools > Mathematics Schools > Computer Science & Informatics
Subjects:	R Medicine > R Medicine (General)
Publisher:	AIS Electronic Library (AISeL)
Date of First Compliant Deposit:	25 January 2019
Date of Acceptance:	24 March 2018
Last Modified:	23 Nov 2022 10:53
URI:	https://orca.cardiff.ac.uk/id/eprint/117771

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