1. Learn
    2. /
  2. Courses
    3. /
  3. Introduction to Natural Language Processing in R
  • 1

    True Fundamentals

    Free

    Chapter 1 of Introduction to Natural Langauge Processing prepares you for running your first analysis on text. You will explore regular expressions and tokenization, two of the most common components of most analysis tasks. With regular expressions, you can search for any pattern you can think of, and with tokenization, you can prepare and clean text for more sophisticated analysis. This chapter is necessary for tackling the techniques we will learn in the remaining chapters of this course.

  • 2

    Representations of Text

    In this chapter, you will learn the most common and studied ways to analyze text. You will look at creating a text corpus, expanding a bag-of-words representation into a TFIDF matrix, and use cosine-similarity metrics to determine how similar two pieces of text are to each other. You build on your foundations for practicing NLP before you dive into applications of NLP in chapters 3 and 4.

  • 3

    Applications: Classification and Topic Modeling

    Chapter 3 focuses on two common text analysis approaches, classification modeling, and topic modeling. If you are working on text analysis projects, you will inevitably use one or both of these methods. This chapter teaches you how to perform both techniques and provides insight into how to approach these techniques from a practical point of you.

  • 4

    Advanced Techniques

    In chapter 4 we cover two staples of natural language processing, sentiment analysis, and word embeddings. These are two analysis techniques that are a must for anyone learning the fundamentals of text analysis. Furthermore, you will briefly learn about BERT, part-of-speech tagging, and named entity recognition. Almost 15 different analysis techniques were covered in this course, so chapter 4 ends by recapping all of the great techniques you will learn about in this course.

Initializing

Exercise

An example of failing at text analysis

Early on, you discussed the power of removing stop words before conducting text analysis. In this most recent chapter, you reviewed using cosine similarity to identify texts that are similar to each other.

In this exercise, you will explore the very real possibility of failing to use text analysis properly. You will compute cosine similarities for the chapters in the book Animal Farm, without removing stop-words.

Instructions

100 XP
  • Review the provided code to create word counts. This has been completed for you.
  • Using the pairwise_similarity() function from widyr, calculate the cosine similarities for each chapter in the chapter column.
  • Arrange the results with the highest similarity values first.
  • Calculate the average mean of the similarity values.