1. Apprendre
    2. /
  2. Cours
    3. /
  3. Introduction to Text Analysis in R
  • 1

    Wrangling Text

    Gratuit

    Since text is unstructured data, a certain amount of wrangling is required to get it into a form where you can analyze it. In this chapter, you will learn how to add structure to text by tokenizing, cleaning, and treating text as categorical data.

  • 2

    Visualizing Text

    While counts are nice, visualizations are better. In this chapter, you will learn how to apply what you know from ggplot2 to tidy text data.

  • 3

    Sentiment Analysis

    While word counts and visualizations suggest something about the content, we can do more. In this chapter, we move beyond word counts alone to analyze the sentiment or emotional valence of text.

  • 4

    Topic Modeling

    In this final chapter, we move beyond word counts to uncover the underlying topics in a collection of documents. We will use a standard topic model known as latent Dirichlet allocation.

Initializing

Exercice

Evaluating a DTM as a matrix

Let's practice casting our tidy data into a DTM and evaluating the DTM by treating it as a matrix.

In this exercise, you will create a DTM again, but with a much smaller subset of the twitter data (tidy_twitter_subset).

Instructions

100 XP
  • Cast the word counts by tweet into a DTM and assign it to dtm_twitter.
  • Coerce dtm_twitter into a matrix called matrix_twitter.
  • Print rows 1 through 5 and columns 90 through 95.