Identifying the high-value social audience from Twitter through text-mining methods

• Main Authors: LO, Siaw Ling, CORNFORTH, David, CHIONG, Raymond
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2014
• Subjects: Twitter; Topic modelling; Machine learning; Audience segmentation; Data Storage Systems; Social Media
• Online Access: https://ink.library.smu.edu.sg/sis_research/4784; https://ink.library.smu.edu.sg/context/sis_research/article/5787/viewcontent/IES2014_highvaluesocialaudience_final.pdf
• Institution: Singapore Management University

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.