Text Mining with Bag-of-Words in R

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

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4 Hours15 Videos69 Exercises38,940 Learners
5700 XP

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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.

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    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

In the following tracks

Text Mining

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.
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