Skip to main content

Text Mining with Bag-of-Words in R

Learn the bag of words technique for text mining with R.

Start Course for Free
4 Hours15 Videos69 Exercises39,248 Learners
5700 XP

Create Your Free Account

GoogleLinkedInFacebook

or

By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA. You confirm you are at least 16 years old (13 if you are an authorized Classrooms user).

Loved by learners at thousands of companies

Course Description

It is estimated that over 70% of potentially usable business information is unstructured, often in the form of text data. Text mining provides a collection of techniques that allows us to derive actionable insights from unstructured data. In this course, we explore the basics of text mining using the bag of words method. The first three chapters introduce a variety of essential topics for analyzing and visualizing text data. The final chapter allows you to apply everything you've learned in a real-world case study to extract insights from employee reviews of two major tech companies.

  1. 1

    Jumping into text mining with bag of words

    Free

    In this chapter, you'll learn the basics of using the bag of words method for analyzing text data.

    Play Chapter Now
    What is text mining?
    50 xp
    Understanding text mining
    50 xp
    Quick taste of text mining
    100 xp
    Getting started
    50 xp
    Load some text
    100 xp
    Make the vector a VCorpus object (1)
    100 xp
    Make the vector a VCorpus object (2)
    100 xp
    Make a VCorpus from a data frame
    100 xp
    Cleaning and preprocessing text
    50 xp
    Common cleaning functions from tm
    100 xp
    Cleaning with qdap
    100 xp
    All about stop words
    100 xp
    Intro to word stemming and stem completion
    100 xp
    Word stemming and stem completion on a sentence
    100 xp
    Apply preprocessing steps to a corpus
    100 xp
    The TDM & DTM
    50 xp
    Understanding TDM and DTM
    50 xp
    Make a document-term matrix
    100 xp
    Make a term-document matrix
    100 xp
  2. 2

    Word clouds and more interesting visuals

    This chapter will teach you how to visualize text data in a way that's both informative and engaging.

    Play Chapter Now
    Common text mining visuals
    50 xp
    Test your understanding of text mining
    50 xp
    Frequent terms with tm
    100 xp
    Frequent terms with qdap
    100 xp
    Intro to word clouds
    50 xp
    A simple word cloud
    100 xp
    Stop words and word clouds
    100 xp
    Plot the better word cloud
    100 xp
    Improve word cloud colors
    100 xp
    Use prebuilt color palettes
    100 xp
    Other word clouds and word networks
    50 xp
    Find common words
    100 xp
    Visualize common words
    100 xp
    Visualize dissimilar words
    100 xp
    Polarized tag cloud
    100 xp
    Visualize word networks
    100 xp
    Teaser: simple word clustering
    100 xp
  3. 3

    Adding to your tm skills

    In this chapter, you'll learn more basic text mining techniques based on the bag of words method.

    Play Chapter Now
    Simple word clustering
    50 xp
    Test your understanding of text mining
    50 xp
    Distance matrix and dendrogram
    100 xp
    Make a dendrogram friendly TDM
    100 xp
    Put it all together: a text-based dendrogram
    100 xp
    Dendrogram aesthetics
    100 xp
    Using word association
    100 xp
    Getting past single words
    50 xp
    N-gram tokenization
    50 xp
    Changing n-grams
    100 xp
    How do bigrams affect word clouds?
    100 xp
    Different frequency criteria
    50 xp
    Changing frequency weights
    100 xp
    Capturing metadata in tm
    100 xp
  4. 4

    Battle of the tech giants for talent

    This chapter ties everything together with a case study in text mining for HR analytics.

    Play Chapter Now
    Amazon vs. Google
    50 xp
    Organizing a text mining project
    50 xp
    Step 1: Problem definition
    50 xp
    Step 2: Identifying the text sources
    100 xp
    Step 3: Text organization
    50 xp
    Text organization
    100 xp
    Working with Google reviews
    100 xp
    Steps 4 & 5: Feature extraction & analysis
    50 xp
    Feature extraction & analysis: amzn_pros
    100 xp
    Feature extraction & analysis: amzn_cons
    100 xp
    amzn_cons dendrogram
    100 xp
    Word association
    100 xp
    Quick review of Google reviews
    100 xp
    Cage match! Amazon vs. Google pro reviews
    100 xp
    Cage match, part 2! Negative reviews
    100 xp
    Step 6: Reach a conclusion
    50 xp
    Draw conclusions, insights, or recommendations
    50 xp
    Draw another conclusion, insight, or recommendation
    50 xp
    Finished!
    50 xp

In the following tracks

Text Mining

Datasets

Coffee tweetsChardonnay tweetsAnonymous online reviews: AmazonAnonymous online reviews: Google

Collaborators

Nick CarchediTom Jeon

Prerequisites

Intermediate R
Ted Kwartler Headshot

Ted Kwartler

Adjunct Professor, Harvard University

Ted Kwartler is the VP, Trusted AI at DataRobot. At DataRobot, Ted sets product strategy for explainable and ethical uses of data technology in the company's application. Ted brings unique insights and experience utilizing data, business acumen and ethics to his current and previous positions at Liberty Mutual Insurance and Amazon. In addition to having 4 DataCamp courses he teaches graduate courses at the Harvard Extension School and is the author of Text Mining in Practice with R.
See More

What do other learners have to say?

“I've used other sites—Coursera, Udacity, things like that—but DataCamp's been the one that I've stuck with.”

Devon Edwards Joseph
Lloyds Banking Group

“DataCamp is the top resource I recommend for learning data science.”

Louis Maiden
Harvard Business School

“DataCamp is by far my favorite website to learn from.”

Ronald Bowers
Decision Science Analytics, USAA