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.

Basis van reguliere expressies

Oefenen met grep-syntaxis

Reguliere-expressiefuncties verkennen.

Tokenisatie

tidytext-functies

Tokenization: zinnen

De basis van tekst opschonen

Tekstvoorbewerking: stopwoorden verwijderen

Voorbewerking van tekst: stemming

True Fundamentals

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. 

Understanding an R corpus

Explore an R corpus

Creating a tibble from a corpus

Creating a corpus

The bag-of-words representation

Practice BoW

BoW Example

Sparse matrices

The TFIDF

Manual calculations

TFIDF Practice

Cosine Similarity

An example of failing at text analysis

Cosine similarity example

Representations of Text

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.

Preparing text for modeling

Data preparation

Removing sparse terms

Classification modeling

Classification modeling example

Confusion matrices

TFIDF tibble vs dtm

Introduction to topic modeling

LDA practice

Assigning topics to documents

LDA in practice

Testing perplexity

Reviewing LDA results

Applications: Classification and Topic Modeling

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. 

Sentiment analysis

tidytext lexicons

Sentiment scores

Sentiment and emotion

Word embeddings

h2o practice

word2vec

Additional NLP analysis

Reviewing methods #1

Review methods #2

Conclusion

Advanced Techniques

Animal Farm

Russian Troll tweets

Zoals bij elke basiscursus is Introductie tot Natural Language Processing in R bedoeld om je de juiste tools te geven om te beginnen met het analyseren van tekst. Natural language processing (NLP) is een snelgroeiend onderdeel van data science, met de afgelopen tien jaar veel spannende ontwikkelingen. In deze cursus behandel je de basis van deze onderwerpen en bereid je je voor om je analysemogelijkheden uit te breiden. We duiken in reguliere expressies, topic modeling, named entity recognition en meer, met duidelijke voorbeelden die je kunt gebruiken om je toekomstige analyses een vliegende start te geven.

Intermediate R

Introduction to the Tidyverse

Ontdek basisvaardigheden en tools voor NLP in R, zoals regex, topic modeling en entiteitsherkenning.

Introductie tot Natural Language Processing in R

Krijg een overzicht van alle vaardigheden en tools die je nodig hebt om goed te worden in natuurlijke taalverwerking in R.

De basis van tekst opschonen

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