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  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

Sparse matrices

During the video lesson you learned about sparse matrices. Sparse matrices can become computational nightmares as the number of text documents and the number of unique words grow. Creating word representations with tweets can easily create sparse matrices because emojis, slang, acronyms, and other forms of language are used.

In this exercise you will walk through the steps to calculate how sparse the Russian tweet dataset is. Note that this is a small example of how quickly text analysis can become a major computational problem.

Instructions 1/4

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  • Use tokenization remove stop-words to find the total number of unique words in the dataset.