Topic Modeling with Word Embeddings

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DC Field	Value	Language
dc.contributor.author	Ahmed, ITBIRENE	-
dc.contributor.author	CHIHANI, Brahim	-
dc.date.accessioned	2022-05-22T14:00:33Z	-
dc.date.available	2022-05-22T14:00:33Z	-
dc.date.issued	2021	-
dc.identifier.uri	https://dspace.univ-ghardaia.edu.dz/xmlui/handle/123456789/1022	-
dc.description.abstract	With the great development in the field of digitization, the extraction of topics through information that is in the form of unmarked texts, is not an easy matter. Therefore, we need a topic modeling technique, which is based on unsupervised algorithms. In our thesis, we clarify the concept of topic modeling and the inherent approaches, such as Latent Dirichlet Allocation (LDA), Embedded Topic Model (ETM), Gaussian LDA (G-LDA), and LDA with Word2Vec (LDA2Vec). In the experimental work, we make an empirical comparison between both LDA and ETM methods on the 20 newsgroups, in terms of runtime and topic coherence. The results are in favor of the ETM method	EN_en
dc.publisher	université Ghardaia	EN_en
dc.subject	topic modeling, topic coherence, Latent Dirichlet Allocation (LDA), Embedded Topic Model (ETM), Gaussian LDA (G-LDA), LDA2Vec.	EN_en
dc.title	Topic Modeling with Word Embeddings	EN_en
dc.type	Thesis	EN_en
Appears in Collections:	Mémoires de Master

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