"Good night, good day, good luck": Applying topic modeling to chat reference transcripts

Megan Ozeran, Piper Martin

Research output: Contribution to journalArticlepeer-review


This article presents the results of a pilot project that tested the application of algorithmic topic modeling to chat reference conversations. The outcomes for this project included determining if this method could be used to identify the most common chat topics in a semester and whether these topics could inform library services beyond chat reference training. After reviewing the literature, four topic modeling algorithms were successfully implemented using Python code: (1) LDA, (2) phrase-LDA, (3) DMM, and (4) NMF. Analysis of the top ten topics from each algorithm indicated that LDA, phrase- LDA, and NMF show the most promise for future analysis on larger sets of data (from three or more semesters) and for examining different facets of the data (fall versus spring semester, different time of day, just the patron side of the conversation).

Original languageEnglish (US)
Pages (from-to)49-57
Number of pages9
JournalInformation Technology and Libraries
Issue number2
StatePublished - Jun 17 2019

ASJC Scopus subject areas

  • Information Systems
  • Library and Information Sciences


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