Estrai le coordinate di geolocalizzazione

Analizzare la geolocalizzazione dei tweet aiuta a influenzare i clienti con campagne di marketing mirate.

Il primo passo per analizzare i dati di geolocalizzazione sulle mappe è estrarre le coordinate disponibili.

Il veganismo è un tema molto promosso. Consiste nell’astenersi dall’uso di prodotti di origine animale e chi lo segue è chiamato "vegano".

In questo esercizio estrarrai le coordinate di geolocalizzazione dai tweet su "#vegan".

La libreria rtweet è già stata caricata per questo esercizio.

Questo esercizio fa parte del corso

Analisi dei dati dei social media in R

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Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

# Extract 1000 tweets on #vegan
vegan <- search_tweets("___", n = ___)

Modifica ed esegui il codice

Questo esercizio fa parte del corso

Analisi dei dati dei social media in R

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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: 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: Analisi delle reti su Twitter Exercise 2: Preparare i dati per una rete di retweet Exercise 3: Crea una rete di retweet Exercise 4: Misure di centralità della rete Exercise 5: Calcola gli out-degree Exercise 6: Calcola i punteggi di in-degree Exercise 7: Calcolare i punteggi di betweenness Exercise 8: Visualizzare le reti di Twitter Exercise 9: Crea un grafico di rete con attributi Exercise 10: Grafico di rete basato sulla misura di centralità Exercise 11: Conteggio dei follower per migliorare il grafico della rete Exercise 12: Mettere i dati di Twitter sulla mappa Exercise 13: Estrai le coordinate di geolocalizzazione

Esercizio in corso

Exercise 14: Dati di Twitter sulla mappa Exercise 15: Chiusura del corso