Haritada Twitter verileri

Harita üzerinde "#vegan" etiketiyle atılan tweet'leri görselleştirmek, en çok hangi bölgelerden paylaşıldıklarını görmek açısından ilginç olacak. Vegan ürünleri tanıtan bir marka, pazarlama çalışmalarında bu bölgelerdeki insanları hedefleyebilir.

Unutma, tüm tweet'lerde jeolokasyon verisi bulunmayabilir; bu bilgi kullanıcılar için isteğe bağlıdır.

Bir önceki egzersizde çıkardığın jeolokasyon koordinatları vegan_coord olarak önceden yüklendi.

Bu egzersiz için maps kütüphanesi de 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.

# Omit rows with missing geo-coordinates in the data frame
vegan_geo <- ___(vegan_coord[,c("___", "___")])

# View the output
head(vegan_geo)

Kodu Düzenle ve Çalıştır

Bu egzersiz

R ile Sosyal Medya Verilerini Analiz Etme

kursunun bir parçasıdır

IntermediárioNível de habilidade

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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: Processing twitter text Exercise 2: Remove URLs and characters other than letters Exercise 3: Build a corpus and convert to lowercase Exercise 4: Remove stop words and additional spaces Exercise 5: Visualize popular terms Exercise 6: Removing custom stop words Exercise 7: Visualize popular terms with bar plots Exercise 8: Word clouds for visualization Exercise 9: Topic modeling of tweets Exercise 10: The LDA algorithm Exercise 11: Create a document term matrix Exercise 12: Create a topic model Exercise 13: Twitter sentiment analysis Exercise 14: Extract sentiment scores Exercise 15: Perform sentiment analysis

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 ağ analizi Exercise 2: Retweet ağı için veriyi hazırlama Exercise 3: Bir retweet ağı oluştur Exercise 4: Ağ merkezilik ölçüleri Exercise 5: Dış-derece (out-degree) puanlarını hesapla Exercise 6: Giriş derecesi puanlarını hesapla Exercise 7: Aradalık (betweenness) skorlarını hesapla Exercise 8: Twitter ağlarını görselleştirme Exercise 9: Özniteliklerle bir ağ grafiği oluştur Exercise 10: Merkezilik ölçüsüne dayalı ağ grafiği Exercise 11: Ağ grafiğini güçlendirmek için takipçi sayısı Exercise 12: Twitter verilerini haritaya yerleştirmek Exercise 13: Coğrafi konum koordinatlarını çıkar Exercise 14: Haritada Twitter verileri

Geçerli Egzersiz

Exercise 15: Kursu tamamlayalım