Topic Modeling as a Tool for Analyzing Library Chat Transcripts.
Library chat services are an increasingly important communication channel to connect patrons to library resources and services. Analysis of chat transcripts could provide librarians with insights into improving services. Unfortunately, chat transcripts consist of unstructured text data, making it im...
| Publicado en: | Information Technology & Libraries Vol. 40; no. 3; pp. 1 - 25 |
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| Autores principales: | , |
| Formato: | tables/charts Journal Article |
| Publicado: |
American Library Association
2021
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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=152572645&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 152572645 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 07309295 ITL jtl: Information Technology & Libraries issn: 07309295 maglogo: N pubinfo: dt: 2021 vid: 40 iid: 3 pid: 55 pub: American Library Association place: Chicago, Illinois artinfo: ui: 152572645 152572645 152572645 10.6017/ital.v40i3.13333 152572645 ppf: 1 ppct: 24 formats: fmt: @attributes: type: P tig: atl: Topic Modeling as a Tool for Analyzing Library Chat Transcripts. aug: au: HyunSeung Koh Mark Fienup affil: Assessment Librarian and Assistant Professor of Library Services, University of Northern Iowa sug: subj: Library Services ab: Library chat services are an increasingly important communication channel to connect patrons to library resources and services. Analysis of chat transcripts could provide librarians with insights into improving services. Unfortunately, chat transcripts consist of unstructured text data, making it impractical for librarians to go beyond simple quantitative analysis (e.g., chat duration, message count, word frequencies) with existing tools. As a stepping-stone toward a more sophisticated chat transcript analysis tool, this study investigated the application of different types of topic modeling techniques to analyze one academic library's chat reference data collected from April 10, 2015, to May 31, 2019, with the goal of extracting the most accurate and easily interpretable topics. In this study, topic accuracy and interpretability--the quality of topic outcomes--were quantitatively measured with topic coherence metrics. Additionally, qualitative accuracy and interpretability were measured by the librarian author of this paper depending on the subjective judgment on whether topics are aligned with frequently asked questions or easily inferable themes in academic library contexts. This study found that from a human's qualitative evaluation, Probabilistic Latent Semantic Analysis (pLSA) produced more accurate and interpretable topics, which is not necessarily aligned with the findings of the quantitative evaluation with all three types of topic coherence metrics. Interestingly, the commonly used technique Latent Dirichlet Allocation (LDA) did not necessarily perform better than pLSA. Also, semi-supervised techniques with human-curated anchor words of Correlation Explanation (CorEx) or guided LDA (GuidedLDA) did not necessarily perform better than an unsupervised technique of Dirichlet Multinomial Mixture (DMM). Last, the study found that using the entire transcript, including both sides of the interaction between the library patron and the librarian, performed better than using only the initial question asked by the library patron across different techniques in increasing the quality of topic outcomes. pubtype: Academic Journal doctype: tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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