Topic Modeling with Word2Vec based Noun Expansion for Dark Web Marketplace Analysis

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dc.contributor.advisorDr. Grootjen, F.A.
dc.contributor.advisorDr. de Boer, M.H.T.(external)
dc.contributor.advisorDr. Joosten, B.(external)
dc.contributor.authorVogelzang, B.
dc.date.issued2019-10-11
dc.description.abstractA novel approach for expanding documents is proposed to improve topic modeling on short text. The enrichment is based on expanding noun words with information from custom (e.g. domain-speci c) and pretrained Word2Vec models. The quality of the di erent conditions: original, custom and pretrained, are evaluated with manual analysis of the created topics and with the classi cation performance of a Suport Vector Machine trained on the output of an LDA system. Manual analysis did not show a striking improvement of the created topics with the enriched texts, compared to the original text. The performance of the prediction models show a improved performance, only when enriched with information from the custom Word2Vec models. However, the extent of the improvement is dependent on the text domain.en_US
dc.embargo.lift10000-01-01
dc.embargo.typePermanent embargoen_US
dc.identifier.urihttps://theses.ubn.ru.nl/handle/123456789/10697
dc.language.isoenen_US
dc.thesis.facultyFaculteit der Sociale Wetenschappenen_US
dc.thesis.specialisationMaster Artificial Intelligenceen_US
dc.thesis.studyprogrammeArtificial Intelligenceen_US
dc.thesis.typeMasteren_US
dc.titleTopic Modeling with Word2Vec based Noun Expansion for Dark Web Marketplace Analysisen_US
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