Aantal retweets

Een retweet helpt je bestaande content te benutten om een following voor je merk op te bouwen.

Hoe vaak een tweet wordt geretweet, laat zien wat trending is. De inzichten die je verzamelt, kun je gebruiken om je merk te promoten met populaire retweets.

In deze oefening ga je tweets over "Artificial Intelligence" identificeren die het vaakst zijn geretweet.

Tweets over "Artificial Intelligence", opgehaald met search_tweets(), zijn opgeslagen als tweets_ai.

De libraries rtweet en dplyr en de gegevensset tweets_ai zijn al ingeladen.

Deze oefening maakt deel uit van de cursus

Socialemediagegevens analyseren in R

Cursus bekijken

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# Create a data frame of tweet text and retweet count
rtwt <- tweets_ai[,c("___", "___")]
head(rtwt)

# Sort data frame based on descending order of retweet counts
rtwt_sort <- arrange(rtwt, desc(___))

Code bewerken en uitvoeren

Deze oefening maakt deel uit van de cursus

Socialemediagegevens analyseren in R

SkillTag.level.intermediateSkillTag.label

4.9+

Begin de cursus gratis

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-gegevens analyseren Exercise 2: Kracht van twitterdata Exercise 3: Voor- en nadelen van Twitter-data Exercise 4: Twitter-gegevens ophalen Exercise 5: Vereisten om de R-omgeving in te richten Exercise 6: Tweets zoeken en extraheren Exercise 7: Zoek en haal tijdlijnen op Exercise 8: Onderdelen van Twitter-gegevens Exercise 9: Interesse van gebruikers en aantal tweets Exercise 10: Volgersaantal vergelijken Exercise 11: Aantal retweets

Huidige oefening

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