Een tibble maken van een corpus

Om de corpus met ruwe-oliedata die je van een collega kreeg verder te verkennen, besluit je een pipeline te maken om de tekst in de documenten op te schonen. In plaats van dit met het tm-pakket te doen, zet je het corpus om naar een tibble zodat je de functies unnest_tokens(), count() en anti_join() kunt gebruiken waar je al mee bekend bent. De corpus crude bevat zowel de metadata als de tekst van elk document.

Deze oefening maakt deel uit van de cursus

Introductie tot Natural Language Processing in R

Cursus bekijken

Oefeninstructies

Zet het corpus om in een tibble.
Gebruik names om de kolomnamen af te drukken.
Tokenize (per woord), tel en verwijder stopwoorden uit de kolom text van crude_tibble.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# Create a tibble & Review
crude_tibble <- ___(crude)
___(crude_tibble)

crude_counts <- crude_tibble %>%
  # Tokenize by word 
  ___(___, text) %>%
  # Count by word
  ___(word, sort = TRUE) %>%
  # Remove stop words
  ___(stop_words)

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: Regular expression basics Exercise 2: Practicing syntax with grep Exercise 3: Exploring regular expression functions.Exercise 4: Tokenization Exercise 5: tidytext functions Exercise 6: Tokenization: sentences Exercise 7: Text cleaning basics Exercise 8: Text preprocessing: remove stop words Exercise 9: Text preprocessing: Stemming

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: Een R-corpus begrijpen Exercise 2: Verken een R-corpus Exercise 3: Een tibble maken van een corpus

Huidige oefening

Exercise 4: Een corpus maken Exercise 5: De bag-of-words-representatie Exercise 6: Oefenen met BoW Exercise 7: BoW-voorbeeld Exercise 8: Sparse matrices Exercise 9: De TFIDF Exercise 10: Handmatige berekeningen Exercise 11: Oefenen met TFIDF Exercise 12: Cosinus-overeenkomst Exercise 13: Een voorbeeld van mislukte tekstanalyse Exercise 14: Voorbeeld van cosinusovereenkomst

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