Open Access“Good Night, Good Day, Good Luck”
Author(s) -
Megan Ozeran,
Piper Martin
Publication year - 2019
Publication title -
information technology and libraries
Language(s) - English
Resource type - Journals
eISSN - 2163-5226
pISSN - 0730-9295
DOI - 10.6017/ital.v38i2.10921
Subject(s) - python (programming language) , computer science , phrase , conversation , world wide web , topic model , information retrieval , natural language processing , artificial intelligence , linguistics , programming language , philosophy
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).