Matriks jarang (sparse matrices)

Dalam video pelajaran, Anda mempelajari tentang matriks jarang. Matriks jarang dapat menjadi mimpi buruk komputasi ketika jumlah dokumen teks dan jumlah kata unik bertambah. Membuat representasi kata dari tweet sangat mudah menghasilkan matriks jarang karena penggunaan emoji, bahasa gaul, akronim, dan bentuk bahasa lainnya.

Dalam latihan ini, Anda akan mengikuti langkah-langkah untuk menghitung seberapa jarang himpunan data tweet berbahasa Rusia. Perhatikan bahwa ini adalah contoh kecil tentang betapa cepatnya analisis teks dapat menjadi masalah komputasi yang besar.

Latihan ini adalah bagian dari kursus

Pengantar Natural Language Processing di R

Lihat Kursus

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Tokenize and remove stop words
tidy_tweets <- russian_tweets %>%
  ___(word, content) %>%
  ___(stop_words)
# Count by word
unique_words <- tidy_tweets %>%
  count(___)

Edit dan Jalankan Kode

Latihan ini adalah bagian dari kursus

Pengantar Natural Language Processing di R

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Mulai Kursus 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: Memahami korpus R Exercise 2: Mengeksplorasi korpus R Exercise 3: Membuat tibble dari korpus Exercise 4: Membuat korpus Exercise 5: Representasi bag-of-words Exercise 6: Latihan BoW Exercise 7: Contoh BoW Exercise 8: Matriks jarang (sparse matrices)

Latihan Saat Ini

Exercise 9: TFIDF Exercise 10: Perhitungan manual Exercise 11: Latihan TFIDF Exercise 12: Kemiripan Kosinus Exercise 13: Contoh kegagalan dalam analisis teks Exercise 14: Contoh cosine similarity

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