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Sentiment Analysis in R
Learn sentiment analysis by identifying positive and negative language, specific emotional intent and making compelling visualizations.
- 4 hours
- 14 Videos
- 52 Exercises
- 6,034 Participants
- 4,200 XP
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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.
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1
Fast & dirty: Polarity scoring
FreeIn the first chapter, you will learn how to apply qdap's sentiment function called polarity() .
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Visualizing sentiment
Make compelling visuals with your sentiment output.
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Sentiment analysis the tidytext way
In the second chapter you will explore 3 subjectivity lexicons from tidytext. Then you will do an inner join to score some text.
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Case study: Airbnb reviews
Is your property a good rental? What do people look for in a good rental?
-
1
Fast & dirty: Polarity scoring
FreeIn the first chapter, you will learn how to apply qdap's sentiment function called polarity() .
-
Sentiment analysis the tidytext way
In the second chapter you will explore 3 subjectivity lexicons from tidytext. Then you will do an inner join to score some text.
-
Visualizing sentiment
Make compelling visuals with your sentiment output.
-
Case study: Airbnb reviews
Is your property a good rental? What do people look for in a good rental?
What do other learners have to say?
“I've used other sites, but DataCamp's been the one that I've stuck with.”
Devon Edwards Joseph
Lloyd's 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
Ted Kwartler
Senior Director, Data Scientist at Liberty Mutual
Ted started his text mining journey at Amazon when he launched the social media customer service team. Since then, he has held analytical leadership roles at startups and Fortune 100 companies. He is the Author of "Text Mining in Practice with R" available at Amazon.
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Collaborators
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Richie Cotton