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Sentiment Analysis in R

Learn sentiment analysis by identifying positive and negative language, specific emotional intent and making compelling visualizations.

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4 Hours14 Videos52 Exercises11,106 Learners4200 XPText Mining Track

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Course Description

Add sentiment analysis to your text mining toolkit! Sentiment analysis is used by text miners in marketing, politics, customer service and elsewhere. In this course you will learn to identify positive and negative language, specific emotional intent, and make compelling visualizations. You will end the course by applying your sentiment analysis skills to Airbnb reviews to learn what makes for a good rental.

  1. 1

    Fast & dirty: Polarity scoring


    In the first chapter, you will learn how to apply qdap's sentiment function called polarity() .

    Play Chapter Now
    Let's talk about our feelings
    50 xp
    Jump right in! Visualize polarity
    100 xp
    TM refresher (I)
    100 xp
    TM refresher (II)
    100 xp
    How many words do YOU know? Zipf's law & subjectivity lexicon
    50 xp
    What is a subjectivity lexicon?
    50 xp
    Where can you observe Zipf's law?
    100 xp
    Polarity on actual text
    100 xp
    Explore qdap's polarity & built-in lexicon
    50 xp
    Happy songs!
    100 xp
    LOL, this song is wicked good
    100 xp
    Stressed Out!
    100 xp

In the following tracks

Text Mining
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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