Topic Modeling in R

via

DataCamp

Course Features

Duration

4 hours

Delivery Method

Online

Available on

Limited Access

Accessibility

Mobile, Desktop, Laptop

Language

English

Subtitles

English

Level

Intermediate

Teaching Type

Self Paced

Video Content

4 hours

Course Description

The course introduces students to topics modelling. This course covers topics modeling, including preparation of corpus and fitting topic models with Latent Dirichlet algorithm in package topicmodels. It also includes visualizing results using ggplot2 and wordclouds.

Course Overview

Virtual Labs

International Faculty

Post Course Interactions

Hands-On Training,Instructor-Moderated Discussions

Skills You Will Gain

Prerequisites/Requirements

Introduction to Natural Language Processing in R

Text Mining with Bag-of-Words in R

What You Will Learn

Learn how to fit topic models using the Latent Dirichlet Allocation algorithm

This course introduces students to the areas involved in topic modeling: preparation of corpus, fitting of topic models using Latent Dirichlet Allocation algorithm (in package topicmodels), and visualizing the results using ggplot2 and wordclouds

Course Content

Expand all sections

Module 1: Quick introduction to the workflow

Module 2: Wordclouds, stopwords, and control arguments

Module 3: Named entity recognition as unsupervised classification

Module 4: How many topics is enough?

Course Instructors

Pavel Oleinikov

Associate Director, Quantitative Analysis Center, Wesleyan University

Pavel Oleinikov uses his background in social and natural sciences to advance the application of quantitative methods to data from the social world. He teaches courses on basics of Big Data, network ...