Congratulations—you have successfully walked through the foundations of NLP and should have a high-level understanding of supervised ML using the NLTK libraries! Sentiment analysis is a fascinating and evolving science that has many different moving parts. I hope this introduction is a good start to your continued research so that you can utilize it in your data analysis. In this chapter, we learned about the various elements of sentiment analysis, such as feature engineering, along with the process of how an NLP ML algorithm works. We also learned how to install NLP libraries in Jupyter to work with unstructured data, along with how to analyze the results created by a classifier model. With this knowledge, we walked through an example of how to use the VADER sentiment analysis model and visualized the results for analysis.
In our last chapter, Chapter 12, Bringing it all Together, we will bring together all the concepts we've covered in this book...
