A directed topic model applied to call center improvement

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dc.contributor.author Afful‐Dadzie, A.
dc.contributor.author Xiong, H.
dc.contributor.author Allen, T.T.
dc.date.accessioned 2019-02-15T11:10:30Z
dc.date.available 2019-02-15T11:10:30Z
dc.date.issued 2015
dc.identifier.other DOI: 10.1002/asmb.2123
dc.identifier.uri http://ugspace.ug.edu.gh/handle/123456789/27558
dc.description.abstract We propose subject matter expert rened topic (SMERT) allocation, a generative probabilistic model applicable to clusteringfreestyle text. SMERT models are three-level hierarchical Bayesian models in which each item is modeled as a nite mixture over aset of topics. In addition to discrete data inputs, we introduce binomial inputs. These ‘high-level’ data inputs permit the ‘boosting’or afrming of terms in the topic denitions and the ‘zapping’ of other terms. We also present a collapsed Gibbs sampler for efcientestimation. The methods are illustrated using real world data from a call center. Also, we compare SMERT with three alternativeapproaches and two criteria. en_US
dc.language.iso en en_US
dc.publisher John Wiley & Sons, Ltd en_US
dc.subject Bayesian modeling en_US
dc.subject Gibbs sampling en_US
dc.subject latent Dirichlet allocation en_US
dc.title A directed topic model applied to call center improvement en_US
dc.type Article en_US


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