Sentiment Analysis in R: The Tidy Way
In this course, you will the learn principles of sentiment analysis from a tidy data perspective.
- 4 hours
- 15 Videos
- 53 Exercises
- 12,298 Participants
- 4,350 XP
Create Your Free Account
By pressing Continue 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 top companies:
Course Description
Text datasets are diverse and ubiquitous, and sentiment analysis provides an approach to understand the attitudes and opinions expressed in these texts. In this course, you will develop your text mining skills using tidy data principles. You will apply these skills by performing sentiment analysis in several case studies, on text data from Twitter to TV news to Shakespeare. These case studies will allow you to practice important data handling skills, learn about the ways sentiment analysis can be applied, and extract relevant insights from real-world data.
-
1
Tweets across the United States
FreeIn this chapter you will implement sentiment analysis using tidy data principles using geocoded Twitter data.
-
Analyzing TV News
Text analysis using tidy principles can be applied to diverse kinds of text, and in this chapter, you will explore a dataset of closed captioning from television news. You will apply the skills you have learned so far to explore how different stations report on a topic with different words, and how sentiment changes with time.
-
Shakespeare gets Sentimental
Your next real-world text exploration uses tragedies and comedies by Shakespeare to show how sentiment analysis can lead to insight into differences in word use. You will learn how to transform raw text into a tidy format for further analysis.
-
Singing a Happy Song (or Sad?!)
In this final chapter on sentiment analysis using tidy principles, you will explore pop song lyrics that have topped the charts from the 1960s to today. You will apply all the techniques we have explored together so far, and use linear modeling to find what the sentiment of song lyrics can predict.
-
1
Tweets across the United States
FreeIn this chapter you will implement sentiment analysis using tidy data principles using geocoded Twitter data.
-
Shakespeare gets Sentimental
Your next real-world text exploration uses tragedies and comedies by Shakespeare to show how sentiment analysis can lead to insight into differences in word use. You will learn how to transform raw text into a tidy format for further analysis.
-
Analyzing TV News
Text analysis using tidy principles can be applied to diverse kinds of text, and in this chapter, you will explore a dataset of closed captioning from television news. You will apply the skills you have learned so far to explore how different stations report on a topic with different words, and how sentiment changes with time.
-
Singing a Happy Song (or Sad?!)
In this final chapter on sentiment analysis using tidy principles, you will explore pop song lyrics that have topped the charts from the 1960s to today. You will apply all the techniques we have explored together so far, and use linear modeling to find what the sentiment of song lyrics can predict.
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
DataCamp Content Creator
Course Instructor
DataCamp offers interactive R, Python, Spreadsheets, SQL and shell courses. All on topics in data science, statistics, and machine learning. Learn from a team of expert teachers in the comfort of your browser with video lessons and fun coding challenges and projects.
Collaborators
-
Richie Cotton