Visualizza i termini più popolari con i grafici a barre

Il grafico a barre è uno strumento semplice ma molto usato nella visualizzazione dei dati.

Permette di riassumere rapidamente categorie e valori in forma visiva.

In questo esercizio creerai grafici a barre per i termini più ricorrenti in un corpus di testo.

Il corpus di testo ripulito che hai creato per "telemedicine" è stato caricato come corp_refined.

Le librerie qdap e ggplot2 sono già state caricate 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 term frequencies for the top 10 words
termfreq_10w <- ___(corp_refined, ___)
termfreq_10w

# Identify terms with more than 60 counts from the top 10 list
term60 <- subset(termfreq_10w, ___ > ___)

# Create a bar plot using terms with more than 60 counts
ggplot(___, aes(x = reorder(___, -___), y = FREQ)) + 
		geom_bar(stat = "identity", fill = "red") + 
		theme(axis.text.x = element_text(angle = 15, hjust = 1))

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: Elaborare il testo di Twitter Exercise 2: Rimuovi URL e caratteri diversi dalle lettere Exercise 3: Crea un corpus e converti in minuscolo Exercise 4: Rimuovi stop word e spazi aggiuntivi Exercise 5: Visualizza i termini più popolari Exercise 6: Rimozione di stop word personalizzate Exercise 7: Visualizza i termini più popolari con i grafici a barre

Esercizio in corso

Exercise 8: Word cloud per la visualizzazione Exercise 9: Topic modeling dei tweet Exercise 10: L'algoritmo LDA Exercise 11: Crea una document term matrix Exercise 12: Crea un topic model Exercise 13: Analisi del sentiment su Twitter Exercise 14: Estrai i punteggi di sentiment Exercise 15: Esegui un'analisi del sentiment

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