Author and Topic Modelling in Text Data

Rasmus Troelsgård

AbstractThis thesis deals with probabilistic modelling of authors, documents, and topics in textual data. The focus is on the Latent Dirichlet Allocation (LDA) model and the Author-Topic (AT) model where Gibbs sampling is used for inferring model parameters from data. Furthermore, a method for optimising hyper parameters in an ML-II setting is described.
Model properties are discussed in connection with applications of the models which include detection of unlikely documents among scientific papers from the NIPS conferences using document perplexity, and the problem of link prediction in the online social network Twitter for which the results are reported as Area Under the ROC curve (AUC) and compared to well known graph-based methods.
TypeMaster's thesis [Academic thesis]
PublisherTechnical University of Denmark, DTU Informatics, E-mail:
AddressAsmussens Alle, Building 305, DK-2800 Kgs. Lyngby, Denmark
NoteSupervised by Professor Lars Kai Hansen,, DTU Informatics
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IMM Group(s)Intelligent Signal Processing

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