Confronta la frequenza dei tweet per due brand

Il volume di tweet pubblicati su un prodotto è un forte indicatore della sua salienza del brand. Confrontiamo la salienza del brand per due marchi concorrenti, Puma e Nike.

Nel precedente esercizio hai creato degli oggetti di serie temporali per i tweet su Puma e Nike. Ora unirai gli oggetti di serie temporali e creerai dei grafici a serie temporali per confrontare la frequenza dei tweet.

Gli oggetti di serie temporali per Puma e Nike sono già stati caricati come puma_ts e nike_ts rispettivamente.

Anche le librerie rtweet, reshape e ggplot2 sono già state caricate.

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.

# Merge the time series objects with "time" as the common column
merged_df <- ___(puma_ts, nike_ts, by = "___", all = TRUE)
head(merged_df)

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: Filtrare i tweet Exercise 2: Filtrare i tweet originali Exercise 3: Filtrare per lingua del tweet Exercise 4: Filtra in base alla popolarità del tweet Exercise 5: Analisi degli utenti di Twitter Exercise 6: Estrai le informazioni sugli utenti Exercise 7: Esplora gli utenti in base al golden ratio Exercise 8: Iscritti alle liste di Twitter Exercise 9: Trend di Twitter Exercise 10: Trend disponibili Exercise 11: Trend per nome del paese Exercise 12: Trend per città e trend più twittati Exercise 13: Rappresentare i dati di Twitter nel tempo Exercise 14: Visualizzare la frequenza dei tweet Exercise 15: Crea oggetti di serie temporali Exercise 16: Confronta la frequenza dei tweet per due brand

Esercizio in corso

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