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Wednesday, March 20 • 10:30am - 11:30am
Analyzing Virtual Reference Transcripts with Machine Learning

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Using open-source machine learning packages, it is possible for librarians to process, analyze, and leverage increasingly large sets of unstructured data. When the appropriate tools and techniques are applied, virtual reference transcripts represent a treasure trove of unstructured data that can provide librarians with information about patrons' needs and insights about how to manage library services. Our research project is ongoing, but so far we have successfully taken a data set consisting of approximately 15,000 transcripts, 3 million words, and 100,000 unique tokens, and have developed a model for automatically processing transcripts, drawing out latent topics using unsupervised learning methods, and clustering transcripts into intuitive groupings. Based on early results, we have identified a number of potential applications for the model with respect to assessing and managing libraries' public services. In particular, librarians and library managers can use these types of machine learning models to improve their staff training processes, challenge underlying assumptions about how patrons engage with library services, and provide an 'early warning' mechanism when a library sees a sudden spike or change in the ways patrons engage with library services.

avatar for Jason Coleman

Jason Coleman

Associate Professor & Department Head, Kansas State University
I am the Head of K-State Libraries' User Services Department. As such I oversee three combined circulation/reference desks as well as the Libraries' Interlibrary Services Unit. I am very interested in user service design, discovery systems, virtual reference, distance library services... Read More →
avatar for Jeremy Walker

Jeremy Walker

Public Services Librarian, Northwestern University

Wednesday March 20, 2019 10:30am - 11:30am CDT
Olin-Rice 150