Introduction to Text Analysis in R
Analyze text data in R using the tidy framework.
Start Course for Free4 Hours15 Videos46 Exercises10,705 Learners
3850 XPCreate Your Free Account
or
By continuing you accept the Terms of Use and Privacy Policy. You also accept that you are aware that your data will be stored outside of the EU and that you are above the age of 16.Loved by learners at thousands of companies
Course Description
From social media to product reviews, text is an increasingly important type of data across applications, including marketing analytics. In many instances, text is replacing other forms of unstructured data due to how inexpensive and current it is. However, to take advantage of everything that text has to offer, you need to know how to think about, clean, summarize, and model text. In this course, you will use the latest tidy tools to quickly and easily get started with text. You will learn how to wrangle and visualize text, perform sentiment analysis, and run and interpret topic models.
- 1
Wrangling Text
FreeSince text is unstructured data, a certain amount of wrangling is required to get it into a form where you can analyze it. In this chapter, you will learn how to add structure to text by tokenizing, cleaning, and treating text as categorical data. - 2
Visualizing Text
While counts are nice, visualizations are better. In this chapter, you will learn how to apply what you know from ggplot2 to tidy text data. - 3
Sentiment Analysis
While word counts and visualizations suggest something about the content, we can do more. In this chapter, we move beyond word counts alone to analyze the sentiment or emotional valence of text. - 4
Topic Modeling
In this final chapter, we move beyond word counts to uncover the underlying topics in a collection of documents. We will use a standard topic model known as latent Dirichlet allocation.
Sumedh Panchadhar
Chester IsmayPrerequisites
Introduction to the Tidyverse
Marc Dotson
Assistant Professor of Marketing, BYU Marriott School of Business
Marc's research is focused on applications of Bayesian inference in marketing, including choice modeling and text analysis. He teaches courses in survey research, conjoint analysis, marketing analytics, and statistical modeling.
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
Join over 7 million learners and start Introduction to Text Analysis in R today!
Create Your Free Account
or
By continuing you accept the Terms of Use and Privacy Policy. You also accept that you are aware that your data will be stored outside of the EU and that you are above the age of 16.