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Introduction to Text Analysis in R

4.5+
13 reviews
Beginner

Analyze text data in R using the tidy framework.

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4 Hours15 Videos46 Exercises
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Course Description

From social media to product reviews, text is an increasingly important type of data across applications, including marketing analytics. In many instances, text is replacing other forms of unstructured data due to how inexpensive and current it is. However, to take advantage of everything that text has to offer, you need to know how to think about, clean, summarize, and model text. In this course, you will use the latest tidy tools to quickly and easily get started with text. You will learn how to wrangle and visualize text, perform sentiment analysis, and run and interpret topic models.
  1. 1

    Wrangling Text

    Free

    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.

    Play Chapter Now
    Text as data
    50 xp
    Airline tweets data
    100 xp
    Grouped summaries
    100 xp
    Counting categorical data
    50 xp
    Counting user types
    100 xp
    Summarizing user types
    100 xp
    Tokenizing and cleaning
    50 xp
    Tokenizing and counting
    100 xp
    Cleaning and counting
    100 xp
  2. 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.

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    Plotting word counts
    50 xp
    Visualizing complaints
    100 xp
    Visualizing non-complaints
    100 xp
    Improving word count plots
    50 xp
    Adding custom stop words
    100 xp
    Visualizing word counts using factors
    100 xp
    Faceting word count plots
    50 xp
    Counting by product and reordering
    100 xp
    Visualizing word counts with facets
    100 xp
    Plotting word clouds
    50 xp
    Creating a word cloud
    100 xp
    Adding a splash of color
    100 xp
  3. 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.

    Play Chapter Now
    Sentiment dictionaries
    50 xp
    Counting the NRC sentiments
    100 xp
    Visualizing the NRC sentiments
    100 xp
    Appending dictionaries
    50 xp
    Counting sentiment
    100 xp
    Visualizing sentiment
    100 xp
    Improving sentiment analysis
    50 xp
    Practicing reshaping data
    100 xp
    Practicing with grouped summaries
    100 xp
    Visualizing sentiment by complaint type
    100 xp
  4. 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.

    Play Chapter Now
    Latent Dirichlet allocation
    50 xp
    Topics as word probabilities
    100 xp
    Summarizing topics
    100 xp
    Visualizing topics
    100 xp
    Document term matrices
    50 xp
    Creating a DTM
    100 xp
    Evaluating a DTM as a matrix
    100 xp
    Running topic models
    50 xp
    Fitting an LDA
    100 xp
    Tidying LDA output
    100 xp
    Comparing LDA output
    100 xp
    Interpreting topics
    50 xp
    Naming three topics
    100 xp
    Naming four topics
    100 xp
    Wrap-up
    50 xp

In the following tracks

Marketing Analytics with RText Mining with R

Datasets

Airline tweetsRoomba reviews

Collaborators

Collaborator's avatar
Chester Ismay
Collaborator's avatar
Sumedh Panchadhar

Prerequisites

Introduction to the Tidyverse
Maham Khan HeadshotMaham Khan

Senior Data Science Content Developer at DataCamp

Hi, I am a Data Scientist and Senior Content Developer at DataCamp, on a mission to make data skills accessible for everyone. Most recently, I've worked on creating toolkits and exploring experimental applications of data science for urban analytics, disaster risk management and climate change mitigation, at the World Bank. I have a background in Experimental Psychology and Philosophy from the University of Oxford, and Urban Data Science from NYU.
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  • Nicolas F.
    13 days

    I really benefited from this class, it set me up for some great job skills that I'm currently using that are helping me tremendously.

  • Seyi A.
    3 months

    It was really helpful to understand text mining. Great content too!

  • Youngkee J.
    4 months

    Thank you.

  • Jonathan L.
    5 months

    Learned a lot from this course. My only complaint is in difficulties when leaving and returning, as some elements (e.g. pre-inserted objects) rely on you either remembering their names from prior lessons or knowing how to use the R console to find out. While this isn't an issue for me, it could be for beginners.

  • Dimitris L.
    9 months

    excellent instructor, good notes

"I really benefited from this class, it set me up for some great job skills that I'm currently using that are helping me tremendously."

Nicolas F.

"It was really helpful to understand text mining. Great content too!"

Seyi A.

"Thank you."

Youngkee J.

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