Word Embedding-based Topic Modeling

DOI

10.2991/978-94-6463-496-9_8How to use a DOI?

Keywords

Topic modelling; Word embeddings; Latent Dirichlet Allocation (LDA); Word2Vec; Topic coherence

Abstract

The extraction of topics from information that is in the form of unmarked texts has become a challenging task due to the significant advancements in the field of digitization. Therefore, we need a topic modeling technique, which is based on unsupervised algorithms. Our paper delineates the topic modeling concept and the inherent approaches including 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 dataset, in terms of topic coherence and runtime. The results are absolutely in favor of the ETM approach.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Slimane Bellaouar
AU  - Ahmed Itbirene
AU  - Brahim Chihani
PY  - 2024
DA  - 2024/08/31
TI  - Word Embedding-based Topic Modeling
BT  - Proceedings of the International Conference on Emerging Intelligent Systems for Sustainable Development (ICEIS 2024)
PB  - Atlantis Press
SP  - 89
EP  - 102
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-496-9_8
DO  - 10.2991/978-94-6463-496-9_8
ID  - Bellaouar2024
ER  -