Seyrek matrisler

Video dersinde seyrek matrisleri öğrendin. Metin belgelerinin sayısı ve benzersiz kelimelerin sayısı arttıkça seyrek matrisler hesaplama açısından kâbusa dönüşebilir. Tweet’lerle kelime temsilleri oluşturmak, emojiler, argo, kısaltmalar ve dilin diğer kullanımları yüzünden kolayca seyrek matrisler yaratır.

Bu egzersizde, Rusça tweet veri kümesinin ne kadar seyrek olduğunu hesaplamak için adımları tek tek uygulayacaksın. Bunun, metin analizinin ne kadar hızlı büyük bir hesaplama problemine dönüşebileceğine dair küçük bir örnek olduğunu unutma.

Bu egzersiz

R ile Doğal Dil İşlemeye Giriş

kursunun bir parçasıdır

Kursu Görüntüle

Uygulamalı interaktif egzersiz

Bu örnek kodu tamamlayarak bu egzersizi bitirin.

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

Kodu Düzenle ve Çalıştır

Bu egzersiz

R ile Doğal Dil İşlemeye Giriş

kursunun bir parçasıdır

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Kursa Ücretsiz Başlayın

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: Bir R derlemesini (corpus) anlamak Exercise 2: Bir R derlemini keşfet Exercise 3: Bir derlemden tibble oluşturma Exercise 4: Bir corpus oluşturma Exercise 5: Bag-of-words gösterimi Exercise 6: BoW Uygulaması Exercise 7: BoW Örneği Exercise 8: Seyrek matrisler

Geçerli Egzersiz

Exercise 9: TFIDF Exercise 10: Manuel hesaplamalar Exercise 11: TFIDF Alıştırması Exercise 12: Kosinüs Benzerliği Exercise 13: Metin analizinde başarısız olmanın bir örneği Exercise 14: Kosinüs benzerliği örneği

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