h2o uygulaması

R'de birden fazla machine learning kütüphanesi mevcut. Ancak h2o kütüphanesi kullanımı kolaydır ve bir word2vec uygulaması sunar. h2o, başka birçok machine learning görevi için de kullanılabilir. Yine de h2o kütüphanesini kullanabilmek için verinde ek ön işleme adımları uygulaman gerekir. Elinde, 2016 ABD seçim kampanyası sırasında otomatik olarak atılan tweet'leri içeren left_right adlı bir veri kümesi var.

Verilerini diğer metin analizi tekniklerine göre hazırlamak yerine, bu veri kümesini h2o kütüphanesi ile kullanılacak şekilde hazırla.

Bu egzersiz

R ile Doğal Dil İşlemeye Giriş

kursunun bir parçasıdır

Kursu Görüntüle

Egzersiz talimatları

Kütüphaneyi içe aktar ve bir h2o oturumu başlat.
Bir h2o nesnesi oluştur.
content sütununda saklanan tweet'leri token'lara ayır.
Kelimeleri küçük harfe dönüştür ve tüm durak sözcükleri kaldır.

Uygulamalı interaktif egzersiz

Bu örnek kodu tamamlayarak bu egzersizi bitirin.

# Initialize an h2o session
library(___)
___.init()

# Create an h2o object for left_right
h2o_object = as.___(left_right)

# Tokenize the words from the column of text in left_right
tweet_words <- h2o.___(h2o_object$___, "\\\\W+")

# Lowercase
tweet_words <- h2o.___(tweet_words)
# Remove stopwords from tweet_words
tweet_words <- tweet_words[is.na(___) || (!___ %in% stop_words$word),]
tweet_words

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: 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: Duygu analizi Exercise 2: tidytext sözlükleri Exercise 3: Duygu puanları Exercise 4: Duygu ve his analizi Exercise 5: Kelime gömme (word embeddings)Exercise 6: h2o uygulaması

Geçerli Egzersiz

Exercise 7: word2vec Exercise 8: Ek NLP analizleri Exercise 9: Yöntemleri gözden geçirme #1 Exercise 10: Yöntemleri gözden geçir #2 Exercise 11: Sonuç