Computationally Analyzing Social Media Text for Topics: A Primer for Advertising Researchers

Joseph T. Yun, Brittany R.L. Duff, Patrick T. Vargas, Hari Sundaram, Itai Himelboim

Research output: Contribution to journalArticlepeer-review


Advertising researchers and practitioners are increasingly using social media analytics (SMA), but focused overviews that explain how to use various SMA techniques are scarce. We focus on how researchers and practitioners can computationally analyze topics of conversation in social media posts, compare each to a human-coded topic analysis of a brand’s Twitter feed, and provide recommendations on how to assess and choose which computational methods to use. The computational methodologies that we survey in this article are text preprocessed summarization, phrase mining, topic modeling, supervised machine learning for text classification, and semantic topic tagging.

Original languageEnglish (US)
Pages (from-to)47-59
Number of pages13
JournalJournal of Interactive Advertising
Issue number1
StatePublished - Jan 2 2020


  • Social media analytics
  • computational advertising
  • machine learning
  • topic discovery
  • topic modeling

ASJC Scopus subject areas

  • Communication
  • Marketing


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