Veri hazırlama

2016 ABD seçimleri sırasında, Rus tweet botları hem demokratlara hem de cumhuriyetçilere sürekli siyasi söylem yaymak için kullanıldı. Sana bu tür tweet’lerden oluşan russian_tweets adlı bir veri kümesi verildi. Bu tweet’leri sol eğilimli (demokrat) veya sağ eğilimli (cumhuriyetçi) olarak sınıflandırmaya karar verdin. Bir sınıflandırma modeli kurmadan önce, metni modelleme için temizlemen ve hazırlaman gerekiyor.

Bu egzersiz

R ile Doğal Dil İşlemeye Giriş

kursunun bir parçasıdır

Kursu Görüntüle

Egzersiz talimatları

Tokenleştirmeyi, token’ları köklerine indirerek tamamla.
Belge-terim matrisi oluşturmak için cast_dtm() kullan.
Belge-terim matrisini tf-idf ağırlıklandırmasıyla ağırlıklandır.
Matrisi yazdır.

Uygulamalı interaktif egzersiz

Bu örnek kodu tamamlayarak bu egzersizi bitirin.

# Stem the tokens
russian_tokens <- russian_tweets %>%
  unnest_tokens(output = "word", token = "words", input = content) %>%
  anti_join(stop_words) %>%
  ___(word = ___(word))

# Create a document term matrix using TFIDF weighting
tweet_matrix <- russian_tokens %>%
  count(tweet_id, word) %>%
  ___(document = ___, term = ___,
           value = n, weighting = tm::___)

# Print the matrix details 
___

Kodu Düzenle ve Çalıştır

Bu egzersiz

R ile Doğal Dil İşlemeye Giriş

kursunun bir parçasıdır

IntermediárioNível de habilidade

4.8+

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: 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: Modelleme için metni hazırlama Exercise 2: Veri hazırlama

Geçerli Egzersiz

Exercise 3: Seyrek terimleri kaldırma Exercise 4: Sınıflandırma modelleme Exercise 5: Sınıflandırma modelleme örneği Exercise 6: Karışıklık matrisleri Exercise 7: TFIDF tibble vs dtm Exercise 8: Konu modellemeye giriş Exercise 9: LDA alıştırması Exercise 10: Konuları belgelere atama Exercise 11: Pratikte LDA Exercise 12: Perpleksiteyi test etme Exercise 13: LDA sonuçlarını gözden geçirme

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