Het LDA-algoritme

Het LDA-model is een wiskundig model dat tegelijkertijd de mix van woorden die bij een topic horen en de mix van topics die elk document beschrijven, schat.

Kies uit de lijst hieronder de invoeren voor de functie LDA() in R:

De tweetcorpus
De document-termmatrix
Aantal topics
Aantal woorden in een topic

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Socialemediagegevens analyseren in R

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Socialemediagegevens analyseren in R

SkillTag.level.intermediateSkillTag.label

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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: 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-tekst verwerken Exercise 2: Verwijder URL's en andere tekens dan letters Exercise 3: Bouw een corpus en zet om naar kleine letters Exercise 4: Stopwoorden en extra spaties verwijderen Exercise 5: Visualiseer populaire termen Exercise 6: Aangepaste stopwoorden verwijderen Exercise 7: Visualiseer populaire termen met staafdiagrammen Exercise 8: Woordwolken voor visualisatie Exercise 9: Topic modeling van tweets Exercise 10: Het LDA-algoritme

Huidige oefening

Exercise 11: Maak een document-term-matrix Exercise 12: Maak een topicmodel Exercise 13: Sentimentanalyse van Twitter Exercise 14: Haal sentimentscores op Exercise 15: Voer sentimentanalyse uit

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