Quantitative Text Analysis and Scaling in R

Offered By
Coursera Project Network
In this Guided Project, you will:

Run an unsupervised document scaling model Plot the output of the unsupervised scaling model

Clock1 hour
BeginnerBeginner
CloudNo download needed
VideoSplit-screen video
Comment DotsEnglish
LaptopDesktop only

By the end of this project, you will learn about the concept of document scaling in textual analysis in R. You will know how to load and pre-process a data set of text documents by converting the data set into a corpus and document feature matrix. You will know how to run an unsupervised document scaling model and explore and plot the scaling outcome.

Skills you will develop

  • Text Analysis
  • Document Scaling
  • Unsupervised Learning
  • Data Visualization (DataViz)
  • Text Corpus

Learn step-by-step

In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:

  1. Load textual data into R and turn it into a corpus object and understand the concept of document scaling in textual analysis

  2. Extract meta-data from text document filenames and subset the data frame to exclude unwanted data

  3. Tokenize and clean the dataset and convert the data into a document feature matrix

  4. Run an unsupervised document scaling model and explore the output

  5. Plot the output of the unsupervised scaling model

How Guided Projects work

Your workspace is a cloud desktop right in your browser, no download required

In a split-screen video, your instructor guides you step-by-step

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Frequently Asked Questions

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