Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

2-2015

Abstract

With the large and growing user base of social media, it is not an easy feat to identify potential customers for business. This is mainly due to the challenge of extracting commercially viable contents from the vast amount of free-form conversations. In this paper, we analyse the Twitter content of an account owner and its list of followers through various text mining methods and segment the list of followers via an index. We have termed this index as the High-Value Social Audience (HVSA) index. This HVSA index enables a company or organisation to devise their marketing and engagement plan according to available resources, so that a high-value social audience can potentially be transformed to customers, and hence improve the return on investment.

Keywords

Twitter, Topic modelling, Machine learning, Audience segmentation.

Discipline

Databases and Information Systems | Social Media

Research Areas

Data Science and Engineering

Publication

Proceedings of the1st Australasian Conference on Artificial Life and Computational Intelligence, ACALCI 2015, Newcastle, Australia, February 5-7, 2015

First Page

323

Last Page

336

ISBN

9783319148021

Identifier

10.1007/978-3-319-14803-8_25

Publisher

Springer

City or Country

Switzerland

Additional URL

https://doi.org/10.1007/978-3-319-14803-8_25

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