1. Wrap-up
Congratulations! You have finished this course on introduction to text analysis in R. In this course, you have built on the foundation of the tidyverse to learn how to wrangle and visualize text, perform sentiment analysis, and run and interpret topic models. While you have come a long way, there is always more to learn!
2. Summary
We have covered a number of new functions to help you wrangle, visualize, and model text data.
You have learned how to tokenize and clean text, remove stop words, and visualize word counts, including using factors to reorder the elements of a plot, faceting, flipping coordinates, and creating word clouds.
You have learned how to conduct sentiment analysis by appending sentiment dictionaries and using tidyr functions to restructure the data and visualize differences in sentiment across groups.
Finally, you have learned how to use topic modeling to uncover underlying themes in text data, including how to create document term matrices and cast to and from tidy formats to visualize and interpret the word probabilities that represent each topic across many possible models.
3. Next steps
With Introduction to the Tidyverse and this course completed, you are ready for a number of other DataCamp courses. Julia Silge's "Sentiment Analysis in R: The Tidy Way" is a deeper dive into all things sentiment analysis. Pavel Oleinikov's "Topic Modeling in R" provides a whole course on topic modeling.
Outside of DataCamp, Julia Silge and David Robinson's book "Text Mining with R," available for free online, provides even more detail on the use of the tidytext package they developed.
4. All the best!
Like I said, there has never been a better time to learn data analysis, especially data analysis in R. Thank you for taking this course and I wish you all the best in your future endeavors.