Durak (stop) kelimeleri ve fazladan boşlukları kaldır

Bir metin korpusu genellikle "a", "an", "the", "of" ve "but" gibi pek çok yaygın kelime içerir. Doğal dil işlemeye göre bunlara stop (durak) kelimeleri denir.

Stop kelimeler, korpustaki daha önemli kelimelere odaklanıp içgörü elde edebilmek için metin işleme sırasında genellikle kaldırılır.

Ayrıca, özel karakterlerin, noktalamanın, sayıların ve stop kelimelerin kaldırılması sırasında oluşan fazladan boşlukların da korpustan temizlenmesi gerekir.

Son egzersizde oluşturduğun korpus twt_corpus_lwr olarak önceden yüklendi.

Bu egzersiz için tm kütüphanesi önceden yüklendi.

Bu egzersiz

R ile Sosyal Medya Verilerini Analiz Etme

kursunun bir parçasıdır

Kursu Görüntüle

Uygulamalı interaktif egzersiz

Bu örnek kodu tamamlayarak bu egzersizi bitirin.

# Remove English stop words from the corpus and view the corpus 
twt_corpus_stpwd <- ___(twt_corpus_lwr, ___, stopwords("___"))
head(twt_corpus_stpwd$content)

Kodu Düzenle ve Çalıştır

Bu egzersiz

R ile Sosyal Medya Verilerini Analiz Etme

kursunun bir parçasıdır

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Kursa Ücretsiz Başlayın

Get started with understanding the power of Twitter data and what you can achieve using social media analysis. In this chapter, you’ll extract your first set of tweets using the Twitter API and functions from the powerful ‘rtweet’ library. Then it’s time to explore how you can use the components from your extracted Twitter data to derive insights for social media analysis.

Exercise 1: Analyzing twitter data Exercise 2: Power of twitter data Exercise 3: Pros and cons of twitter data Exercise 4: Extracting twitter data Exercise 5: Prerequisites to set up the R environment Exercise 6: Search and extract tweets Exercise 7: Search and extract timelines Exercise 8: Components of twitter data Exercise 9: User interest and tweet counts Exercise 10: Compare follower count Exercise 11: Retweet counts

It’s time to go deeper. Learn how you can apply filters to tweets and analyze Twitter user data using the golden ratio and the Twitter lists they subscribe to. You’ll also learn how to extract trending topics and analyze Twitter data over time to identify interesting insights.

Exercise 1: Filtering tweets Exercise 2: Filtering for original tweets Exercise 3: Filtering on tweet language Exercise 4: Filter based on tweet popularity Exercise 5: Twitter user analysis Exercise 6: Extract user information Exercise 7: Explore users based on the golden ratio Exercise 8: Subscribers to twitter lists Exercise 9: Twitter trends Exercise 10: Available trends Exercise 11: Trends by country name Exercise 12: Trends by city and most tweeted trends Exercise 13: Plotting twitter data over time Exercise 14: Visualizing frequency of tweets Exercise 15: Create time series objects Exercise 16: Compare tweet frequencies for two brands

A picture is worth a thousand words! In this chapter, you’ll discover how you can visualize text from tweets using bar plots and word clouds. You’ll learn how to process tweet text and prepare a clean text corpus for analysis. Imagine being able to extract key discussion topics and people's perceptions about a subject or brand from the tweets they are sharing. You’ll be able to do just that using topic modeling and sentiment analysis.

Exercise 1: Twitter metnini işleme Exercise 2: URL'leri ve harf dışındaki karakterleri kaldır Exercise 3: Bir corpus oluştur ve küçük harfe dönüştür Exercise 4: Durak (stop) kelimeleri ve fazladan boşlukları kaldır

Geçerli Egzersiz

Exercise 5: Popüler terimleri görselleştir Exercise 6: Özel durak sözcüklerini kaldırma Exercise 7: Popüler terimleri çubuk grafiklerle görselleştir Exercise 8: Görselleştirme için kelime bulutları Exercise 9: Tweet'lerde konu modelleme Exercise 10: LDA algoritması Exercise 11: Bir belge-terim matrisi oluştur Exercise 12: Bir konu modeli oluştur Exercise 13: Twitter duygu analizi Exercise 14: Duygu puanlarını çıkar Exercise 15: Duygu analizi yap

Twitter users tweet, like, follow, and retweet creating complex network structures. In this final chapter, you’ll learn how to analyze these network structures and visualize the relationships between these individual people as a retweet network. By extracting geolocation data from the tweets you’ll also discover how to display tweet locations on a map, and answer powerful questions such as which states or countries are talking about your brand the most? Geographic data adds a new dimension to your Twitter data analysis.

Exercise 1: Twitter network analysis Exercise 2: Preparing data for a retweet network Exercise 3: Create a retweet network Exercise 4: Network centrality measures Exercise 5: Calculate out-degree scores Exercise 6: Compute the in-degree scores Exercise 7: Calculate the betweenness scores Exercise 8: Visualizing twitter networks Exercise 9: Create a network plot with attributes Exercise 10: Network plot based on centrality measure Exercise 11: Follower count to enhance the network plot Exercise 12: Putting twitter data on the map Exercise 13: Extract geolocation coordinates Exercise 14: Twitter data on the map Exercise 15: Course wrap-up