Fast vectorization, topic modeling, distances and GloVe word embeddings in R.
An R package for Keyword Assisted Topic Models
R Shiny app for tweet analysis
The objective of this project is to monitor the trends in data science job opportunities. We achieve this through scraping of the jobserve website.
PsychTopics – A Shiny App for Exploring and Analyzing Research Topics in Psychology
Optimal topic identification from a pool of Latent Dirichlet Allocation models
A rolling version of the Latent Dirichlet Allocation.
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
Sentiment Analysis of Twitter Data (saotd)
Determine a Prototype from a number of runs of Latent Dirichlet Allocation.
Practices and Tools of Open Science: Topic Modeling
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…
LDA models parameters tuning
An interactive shiny application demonstrating LDA - Latent Dirichlet Allocation
textRec utlizes Latent Dirichlet Allocation and Jensen-Shannon-Divergence on the discrete probability distributions over LDA topics per document, in order to recommend unique and novel documents to specific users.
This project uses latent dirichlet allocation for topic modeling. It first generates the document term matrix. Then It builds the latent dirichlet allocation algorithm to extract latent topics in the documents. A wordcloud function is also implemented to display the representative words of each topics.
Final project for Text Mining course.
Latent Dirichlet Allocation Topic Sub-Modelling Using RStudio. The objective of this task is to ingest crawled tweets on a particular topic and then divide them into major sub-topics based on their relevance and frequency
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