Zaman serisi nesneleri oluştur

Bir zaman serisi nesnesi, belirli bir zaman aralığı boyunca tweet'lerin toplanmış sıklığını içerir.

Zaman serisi nesneleri oluşturmak, karşılaştırma için tweet sıklıklarını görselleştirmeden önceki ilk adımdır.

Bu egzersizde, rakip spor giyim markaları Puma ve Nike için zaman serisi nesneleri oluşturacaksın.

search_tweets() kullanılarak "#puma" ve "#nike" için çıkarılan tweet'ler, puma_st ve nike_st olarak senin için 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.

# Create a time series object for Puma at hourly intervals
puma_ts <- ___(puma_st, by ='___')

# Rename the two columns in the time series object
names(puma_ts) <- c("time", "puma_n")

# View the output
head(puma_ts)

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: Tweet’leri filtreleme Exercise 2: Orijinal tweet’leri filtreleme Exercise 3: Tweet diline göre filtreleme Exercise 4: Tweet popülerliğine göre filtrele Exercise 5: Twitter kullanıcı analizi Exercise 6: Kullanıcı bilgilerini çıkar Exercise 7: Altın orana göre kullanıcıları keşfet Exercise 8: Twitter listelerinin aboneleri Exercise 9: Twitter trendleri Exercise 10: Mevcut trendler Exercise 11: Ülke adına göre trendler Exercise 12: Şehirlere göre trendler ve en çok tweetlenen trendler Exercise 13: Twitter verilerini zaman içinde görselleştirme Exercise 14: Tweet sıklığını görselleştirme Exercise 15: Zaman serisi nesneleri oluştur

Geçerli Egzersiz

Exercise 16: İki marka için tweet sıklıklarını karşılaştır

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 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