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

Abaho, Michael, Gartner, Daniel, Cerutti, Federico 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. 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)
Status: Published
Schools: Mathematics
Computer Science & Informatics
Subjects: R Medicine > R Medicine (General)
Date of First Compliant Deposit: 25 January 2019
Date of Acceptance: 24 March 2018
Last Modified: 25 Jan 2019 16:45
URI: http://orca.cf.ac.uk/id/eprint/117771

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