This course is part of these tracks:
Julia Silge
Data Scientist at Stack Overflow
Julia has a PhD in astrophysics and an abiding love for Jane Austen. She worked in academia and ed tech before moving into data science and discovering R. She enjoys making beautiful charts, programming in R, and communicating about technical topics with diverse audiences. Her interests include data science and visualization, text mining , and teaching. She, with co-author David Robinson, has written Text Mining with R.
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Richie Cotton
Prerequisites
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.
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1
Tweets across the United States
FreeIn this chapter you will implement sentiment analysis using tidy data principles using geocoded Twitter data.
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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.
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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.
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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.
