Voorbewerking van tekst: stemming

De stam van woorden is vaak belangrijker dan hun uitgangen, zeker bij tekstanalyse. Het boek Animal Farm gaat duidelijk over dieren. Maar weten dat het boek animal's 248 keer en animal 107 keer noemt, helpt je analyse misschien niet echt verder.

tidy_animal_farm bevat een tibble met de woorden uit Animal Farm, getokenized en zonder stopwoorden. De volgende stap is om de woorden te stemmen en de resultaten te verkennen.

Deze oefening maakt deel uit van de cursus

Introductie tot Natural Language Processing in R

Cursus bekijken

Oefeninstructies

Gebruik dplyr en SnowballC om de woorden uit tidy_animal_farm te stemmen.
Print de oude woordfrequenties uit tidy_animal_farm.
Print de nieuwe woordfrequenties uit stemmed_animal_farm.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# Perform stemming on tidy_animal_farm
stemmed_animal_farm <- tidy_animal_farm %>%
  ___(word = ___(___))

# Print the old word frequencies 
___ %>%
  ___(word, sort = ___)

# Print the new word frequencies
___ %>%
  ___(word, sort = ___)

Code bewerken en uitvoeren

Deze oefening maakt deel uit van de cursus

Introductie tot Natural Language Processing in R

SkillTag.level.intermediateSkillTag.label

4.8+

Begin de cursus gratis

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.

Exercise 1: Basis van reguliere expressies Exercise 2: Oefenen met grep-syntaxis Exercise 3: Reguliere-expressiefuncties verkennen.Exercise 4: Tokenisatie Exercise 5: tidytext-functies Exercise 6: Tokenization: zinnen Exercise 7: De basis van tekst opschonen Exercise 8: Tekstvoorbewerking: stopwoorden verwijderen Exercise 9: Voorbewerking van tekst: stemming

Huidige oefening

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.

Exercise 1: Understanding an R corpus Exercise 2: Explore an R corpus Exercise 3: Creating a tibble from a corpus Exercise 4: Creating a corpus Exercise 5: The bag-of-words representation Exercise 6: Practice BoW Exercise 7: BoW Example Exercise 8: Sparse matrices Exercise 9: The TFIDF Exercise 10: Manual calculations Exercise 11: TFIDF Practice Exercise 12: Cosine Similarity Exercise 13: An example of failing at text analysis Exercise 14: Cosine similarity example

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.

Exercise 1: Preparing text for modeling Exercise 2: Data preparation Exercise 3: Removing sparse terms Exercise 4: Classification modeling Exercise 5: Classification modeling example Exercise 6: Confusion matrices Exercise 7: TFIDF tibble vs dtm Exercise 8: Introduction to topic modeling Exercise 9: LDA practice Exercise 10: Assigning topics to documents Exercise 11: LDA in practice Exercise 12: Testing perplexity Exercise 13: Reviewing LDA results

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.

Exercise 1: Sentiment analysis Exercise 2: tidytext lexicons Exercise 3: Sentiment scores Exercise 4: Sentiment and emotion Exercise 5: Word embeddings Exercise 6: h2o practice Exercise 7: word2vec Exercise 8: Additional NLP analysis Exercise 9: Reviewing methods #1 Exercise 10: Review methods #2 Exercise 11: Conclusion