Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

12-2014

Abstract

The ability to identify or predict a target audience from the increasingly crowded social space will provide a company some competitive advantage over other companies. In this paper, we analyze various training datasets, which include Twitter contents of an account owner and its list of followers, using features generated in different ways for two machine learning approaches - the Extreme Learning Machine (ELM) and Support Vector Machine (SVM). Various configurations of the ELM and SVM have been evaluated. The results indicate that training datasets using features generated from the owner tweets achieve the best performance, relative to other feature sets. This finding is important and may aid researchers in developing a classifier that is capable of identifying a specific group of target audience members. This will assist the account owner to spend resources more effectively, by sending offers to the right audience, and hence maximize marketing efficiency and improve the return on investment.

Keywords

Extreme learning machine, Support vector machine, Machine learning, Target audience, Twitter, Social media

Discipline

Data Storage Systems

Research Areas

Data Science and Engineering

Publication

Proceedings of the 5th International Conference on Extreme Learning Machines, Singapore, 2014 December 10-12

Volume

1

First Page

417

Last Page

434

Identifier

10.1007/978-3-319-14063-6_35

Publisher

Springer Link

City or Country

Singapore

Additional URL

https://doi.org/10.1007/978-3-319-14063-6_35

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