Pre-elaborazione del testo: stemming

La radice delle parole è spesso più importante delle desinenze, soprattutto nell’analisi del testo. Il libro Animal Farm è chiaramente incentrato sugli animali. Tuttavia, sapere che il libro menziona animal's 248 volte e animal 107 volte potrebbe non essere utile per la tua analisi.

tidy_animal_farm contiene una tibble con le parole di Animal Farm, tokenizzate e senza stop word. Il prossimo passo è applicare lo stemming alle parole ed esplorare i risultati.

Questo esercizio fa parte del corso

Introduzione all'Elaborazione del Linguaggio Naturale in R

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Istruzioni dell'esercizio

Usa dplyr e SnowballC per applicare lo stemming alle parole di tidy_animal_farm.
Stampa le vecchie frequenze delle parole da tidy_animal_farm.
Stampa le nuove frequenze delle parole da stemmed_animal_farm.

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

# 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 = ___)

Modifica ed esegui il codice

Questo esercizio fa parte del corso

Introduzione all'Elaborazione del Linguaggio Naturale in R

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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: Nozioni di base sulle espressioni regolari Exercise 2: Esercitarsi con la sintassi di grep Exercise 3: Esplorare le funzioni per le espressioni regolari.Exercise 4: Tokenizzazione Exercise 5: funzioni di tidytext Exercise 6: Tokenizzazione: frasi Exercise 7: Nozioni di base sulla pulizia del testo Exercise 8: Pre-elaborazione del testo: rimuovere le stop word Exercise 9: Pre-elaborazione del testo: stemming

Esercizio in corso

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