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

Research output: Contribution to journalArticle

Abstract

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)
JournalJournal of Interactive Advertising
DOIs
StateAccepted/In press - Jan 1 2019

Keywords

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

ASJC Scopus subject areas

  • Communication
  • Marketing

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