Sentiment Analysis in R
Learn sentiment analysis by identifying positive and negative language, specific emotional intent and making compelling visualizations.
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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
Fast & dirty: Polarity scoring
FreeIn the first chapter, you will learn how to apply qdap's sentiment function called polarity() . - 2
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. - 4
Case study: Airbnb reviews
Is your property a good rental? What do people look for in a good rental?
In the following tracks
Text MiningDatasets
Line by line polarity for 4 books4 books as a tidy data frame4 books as DocumentTermMatricesPolarity scores of Boston Airbnb reviewsHousing rental reviews from Airbnb in BostonPrerequisites
Text Mining with Bag-of-Words in R
EDWARD 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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Lloyds Banking Group
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Harvard Business School
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Ronald Bowers
Decision Science Analytics, USAA
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