Module 36 Sentiment analysis with Harry Potter

 

1. Start a new R file Name it “harry.R”.

2. Set up your work space by loading the ggplot2, dplyr, tidytext, gsheet, wordcloud2, sentimentr, and lubridate packages.

3. Read in your Harry Potter data by running the following:

hp <- read_csv('https://raw.githubusercontent.com/databrew/intro-to-data-science/main/data/harrypotter.csv')

4. Take a peek at the first few rows of the data. What is the unit of observation?

5. Make all of the text column be lower case.

6. Make all of the text column be upper case.

7. Use unnest_tokens to create a dataframe with one row per word.

8. Create a variable named word_length with the number of characters in each word.

9. Make a histogram of word length.

10. Make a density chart of word length.

11. Make the density chart of word length have a different fill for each chapter.

12. Get the average word length per chapter.

13. Plot the average word length per chapter.

14. What is the longest word used in Harry Potter?

15. What is the most frequent word used in Harry Potter?

16. Get the number of words per chapter.

17. Plot the number of words per chapter.

18. What’s the longest chapter in Harry Potter?

19. Run the below to create an object named sw.

sw <- read_csv('https://raw.githubusercontent.com/databrew/intro-to-data-science/main/data/stopwords.csv')

20. Remove the stop words form your one-row-per-word dataframe.

21. What is the most frequently used (non-stop) word in Harry Potter?

22. Create an object called sentiments by running the following:

sentiments <- get_sentiments('afinn')

23. Use left_join to bring a sentiment classification to each word.

24. Is Harry Potter more negative or positive?

25. Calculate the average sentimentality per chapter.

26. Plot the average sentimentality per chapter.

27. Create a variable called cumulative_sentiment. Use cumsum to get the cumulative sum sentimentality.

28. Plot cumulative sentiment.

29. Color your plot by chapter.