Trends op landnaam

Plaatsgebonden trends laten populaire onderwerpen zien die in een specifieke locatie trending zijn. Je kunt trends op landniveau of stadsniveau ophalen.

Het is zinvoller om trends rond een specifieke regio te bekijken, zodat je je kunt richten op het Twitter-publiek in die regio voor gerichte marketing van een merk.

Kun je onderwerpen ophalen die in Canada trending zijn en de trends bekijken?

De bibliotheek rtweet is voor deze oefening alvast geladen.

Deze oefening maakt deel uit van de cursus

Socialemediagegevens analyseren in R

Cursus bekijken

Oefeninstructies

Haal onderwerpen op die in Canada trending zijn.
Bekijk de eerste 6 kolommen van de opgehaalde gegevens.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# Get topics trending in Canada
gt_country <- ___("___")


# View the first 6 columns
head(___[,1:6])

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: 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: Tweets filteren Exercise 2: Filteren op originele tweets Exercise 3: Filteren op taal van tweets Exercise 4: Filteren op populariteit van tweets Exercise 5: Analyse van Twitter-gebruikers Exercise 6: Gebruikersinformatie extraheren Exercise 7: Verken gebruikers op basis van de golden ratio Exercise 8: Abonnees op Twitter-lijsten Exercise 9: Twitter-trends Exercise 10: Beschikbare trends Exercise 11: Trends op landnaam

Huidige oefening

Exercise 12: Trends per stad en meest getweete trends Exercise 13: Twitter-gegevens in de tijd plotten Exercise 14: Frequentie van tweets visualiseren Exercise 15: Maak tijdreeksobjecten Exercise 16: Vergelijk tweetfrequenties voor twee merken

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