Introduction to Topic Modelling in R
Offered By: Coursera Project Network via Coursera
Course Description
Overview
By the end of this project, you will know how to load and pre-process a data set of text documents by converting the data set into a document feature matrix and reducing it’s dimensionality. You will also know how to run an unsupervised machine learning LDA topic model (Latent Dirichlet Allocation). You will know how to plot the change in topics over time as well as explore the distribution of topic probability in each document.
Syllabus
- Project Overview
- By the end of this project, you will know how to load and pre-process a data set of text documents by converting the data set into a document feature matrix and reducing it’s dimensionality. You will also know how to run an unsupervised machine learning LDA topic model (Latent Dirichlet Allocation). You will know how to plot the change in topics over time as well as explore the distribution of topic probability in each document. This project is aimed at beginners who have a basic familiarity with the statistical programming language R and the RStudio environment, or people with a small amount of experience who would like to learn how to apply topic modelling on textual data.
Taught by
Nicole Baerg
Related Courses
Поиск структуры в данныхMoscow Institute of Physics and Technology via Coursera Applied Text Mining in Python
University of Michigan via Coursera Hands-on Text Mining and Analytics
Yonsei University via Coursera Optimization of Topic Models using Grid Search Method
Coursera Project Network via Coursera Natural Language Processing and Capstone Assignment
University of California, Irvine via Coursera