Twitter verisinin gücü

Twitter’da her saniye paylaşılan tweet’lerin hacmi ve hızı, twitter verisinin gücünün bir göstergesidir.

Tweet metninden metaverisine kadar mevcut devasa bilgi miktarı, çıkarılan tweet’leri analiz etmek ve içgörü elde etmek için büyük bir olanak sunar.

Hadi stream_tweets() kullanarak 120 saniyelik bir pencerede canlı tweet’lerin rastgele %1 örneğini çıkaralım ve bunu bir veri çerçevesine kaydedelim.

Veri çerçevesinin boyutları, kaç canlı tweet’in çıkarıldığına ve tweet metni ile tweet’lere ait metaveriyi içeren kaç sütun olduğuna dair fikir verecek.

Bu egzersiz

R ile Sosyal Medya Verilerini Analiz Etme

kursunun bir parçasıdır

Kursu Görüntüle

Egzersiz talimatları

120 saniyelik bir zaman aralığı için canlı tweet’leri çıkar.
Çıkarılan tweet’lerin bulunduğu veri çerçevesinin boyutlarını görüntüle.

Uygulamalı interaktif egzersiz

Bu örnek kodu tamamlayarak bu egzersizi bitirin.

# Extract live tweets for 120 seconds window
tweets120s <- stream_tweets("", timeout = ___)

# View dimensions of the data frame with live tweets
dim(___)

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: Twitter verilerini analiz etme Exercise 2: Twitter verisinin gücü

Geçerli Egzersiz

Exercise 3: Twitter verisinin artıları ve eksileri Exercise 4: Twitter verisi çıkarma Exercise 5: R ortamını kurmak için önkoşullar Exercise 6: Tweet ara ve çıkar Exercise 7: Zaman akışlarını ara ve çıkar Exercise 8: Twitter verisinin bileşenleri Exercise 9: Kullanıcı ilgisi ve tweet sayıları Exercise 10: Takipçi sayısını karşılaştır Exercise 11: Retweet sayıları

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 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