Topic Modelling in Semantic Embedding Spaces
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Updated
Nov 26, 2025 - R
Topic Modelling in Semantic Embedding Spaces
Various examples of topic modeling and other text analysis
Latent Dirichlet Allocation coupled with Bayesian Time Series analyses
Collect Twitter data and create topic models with R
My version of topic modelling using Latent Dirichlet Allocation (LDA) which finds the best number of topics for a set of documents using ldatuning package which comes with different metrics
A rolling version of the Latent Dirichlet Allocation.
Optimal topic identification from a pool of Latent Dirichlet Allocation models
The repo contains the main topics carried out in my master's thesis on operational risk. In particular, it is described how to implement the so called Loss Distribution Approach (LDA), which is considered the state-of-the-art method to compute capital charge among large banks.
Determine a Prototype from a number of runs of Latent Dirichlet Allocation.
R codes for common Machine Learning Algorithms
Demonstration of a standard topic model approach
Protests and agitations have long used as means for showing dissident towards social, political and economic issues in civil societies. In recent years we have witnessed a large number of protests across various geographies. Not to be left behind by similar trends in the rest of the world, South Africa, in recent years have witnessed a large num…
Sign Language Digit Classification
Classic Machine Learning in R
Correlated topic modeling
Sequencing and interpreting multiple distribution signals
Introduction to Data Mining
Topic model of curriculum of liberal arts program (with shiny app)
Trait estimation and taxonomic discrimination of herbarium leaves with spectra.
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