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"Good Night, Good Day, Good Luck": Applying Topic Modeling to Chat Reference Transcripts.

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Titel: "Good Night, Good Day, Good Luck": Applying Topic Modeling to Chat Reference Transcripts.
Autoren: Ozeran, Megan, Martin, Piper
Quelle: Information Technology & Libraries; Jun2019, Vol. 38 Issue 2, p59-67, 9p, 4 Charts
Schlagwörter: ACADEMIC libraries, ALGORITHMS, INFORMATION retrieval, LIBRARY reference services, RESEARCH funding, TEXT messages, ACCESS to information, MEDICAL coding, STATISTICAL models
Geografische Kategorien: ILLINOIS
Abstract: 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). [ABSTRACT FROM AUTHOR]
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Datenbank: Complementary Index
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Abstract: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). [ABSTRACT FROM AUTHOR]
ISSN:07309295
DOI:10.6017/ital.v38i2.10921