K-Means Subject Matter Expert Refined Topic Model Methodology

K-Means Subject Matter Expert Refined Topic Model Methodology

We propose an innovative technique using K-means clustering to estimate the posterior topic distributions in Latent Dirichlet topic models as an alternative to the collapsed Gibbs sampling technique. This research also develops a topic modeling software instantiation of the K-means Subject Matter Ex...

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Bibliographische Detailangaben
Hauptverfasser: Parker,Nathan, Allen,Theodore T, Sui,Zhenhuan
Format: Report
Sprache:eng
Schlagworte:
clustering
K-means
KSMERT
Latent Dirichlet Allocation
LDA
SMERT
Subject Matter Expert Refined Topic
Text Analysis
Topic Models
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Beschreibung
Zusammenfassung:We propose an innovative technique using K-means clustering to estimate the posterior topic distributions in Latent Dirichlet topic models as an alternative to the collapsed Gibbs sampling technique. This research also develops a topic modeling software instantiation of the K-means Subject Matter Expert Refined Topic methodology using the Visual Basic for Applications programming language. This topic modeling software is deployable across the majority of the Department of Defense computing environments and allows analysts to develop topic models using a graphical user interface.