Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

11-2014

Abstract

Doing business on social media has become a common practice for many companies these days. While the contents shared on Twitter and Facebook offer plenty of opportunities to uncover business insights, it remains a challenge to sift through the huge amount of social media data and identify the potential social audience who is highly likely to be interested in a particular company. In this paper, we analyze the Twitter content of an account owner and its list of followers through various text mining methods, which include fuzzy keyword matching, statistical topic modeling and machine learning approaches. We use tweets of the account owner to segment the followers and identify a group of high-value social audience members. This enables the account owner to spend resources more effectively by sending offers to the right audience and hence maximize marketing efficiency and improve the return of investment.

Keywords

Twitter, Topic modelling, Machine learning, Audience segmentation

Discipline

Data Storage Systems | Social Media

Research Areas

Data Science and Engineering

Publication

Proceedings of the 18th Asia Pacific Symposium on Intelligent and Evolutionary Systems, Singapore, 2014 November 10-12

First Page

325

Last Page

339

ISBN

9783319133591

Identifier

10.1007/978-3-319-13359-1_26

Publisher

Springer Link

City or Country

Singapore

Additional URL

https://doi.org/10.1007/978-3-319-13359-1_26

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