"Good Night, Good Day, Good Luck": Applying Topic Modeling to Chat Reference Transcripts.
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 topi...
| Publicado en: | Information Technology & Libraries Vol. 38; no. 2; pp. 59 - 68 |
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| Autores principales: | , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
American Library Association
Jun2019
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=137208930&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 137208930 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 07309295 ITL jtl: Information Technology & Libraries issn: 07309295 maglogo: N pubinfo: dt: Jun2019 vid: 38 iid: 2 pid: 55 pub: American Library Association place: Chicago, Illinois artinfo: ui: 137208930 137208930 137208930 10.6017/ital.v38i2.10921 137208930 ppf: 59 ppct: 9 formats: fmt: @attributes: type: P tig: atl: "Good Night, Good Day, Good Luck": Applying Topic Modeling to Chat Reference Transcripts. aug: au: Ozeran, Megan Martin, Piper affil: Data Analytics & Visualization Librarian, University of Illinois Library sug: subj: Library Reference Services Models, Statistical Algorithms Text Messaging Human Illinois Information Retrieval Libraries, Academic Illinois Coding Access to Information Funding Source ab: 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). pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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