Understanding Topic Models in Context: A Mixed-Methods Approach to the Meaningful Analysis of Large Document Collections

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dc.contributor.authorEickhoff, Matthias
dc.contributor.authorWieneke, Runhild
dc.date.accessioned2017-12-28T00:42:13Z
dc.date.available2017-12-28T00:42:13Z
dc.date.issued2018-01-03
dc.description.abstractIn recent years, we have witnessed an unprecedented proliferation of large document collections. This development has spawned the need for appropriate analytical means. In particular, to seize the thematic composition of large document collections, researchers increasingly draw on quantitative topic models. Among their most prominent representatives is the Latent Dirichlet Allocation (LDA). Yet, these models have significant drawbacks, e.g. the generated topics lack context and thus meaningfulness. Prior research has rarely addressed this limitation through the lens of mixed-methods research. We position our paper towards this gap by proposing a structured mixed-methods approach to the meaningful analysis of large document collections. Particularly, we draw on qualitative coding and quantitative hierarchical clustering to validate and enhance topic models through re-contextualization. To illustrate the proposed approach, we conduct a case study of the thematic composition of the AIS Senior Scholars' Basket of Journals.
dc.format.extent10 pages
dc.identifier.doi10.24251/HICSS.2018.113
dc.identifier.isbn978-0-9981331-1-9
dc.identifier.urihttp://hdl.handle.net/10125/50000
dc.language.isoeng
dc.relation.ispartofProceedings of the 51st Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectData, Text and Web Mining for Business Analytics
dc.subjectTopic Modelling, Mixed Methdos, LDA, Topic Coding, Textual Data
dc.titleUnderstanding Topic Models in Context: A Mixed-Methods Approach to the Meaningful Analysis of Large Document Collections
dc.typeConference Paper
dc.type.dcmiText

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