Bandingkan frekuensi tweet untuk dua merek

Jumlah tweet yang diposting tentang suatu produk adalah indikator kuat dari saliens merek. Mari bandingkan saliens merek untuk dua merek yang bersaing, Puma dan Nike.

Pada latihan sebelumnya, Anda telah membuat objek deret waktu untuk tweet tentang Puma dan Nike. Anda akan menggabungkan objek deret waktu tersebut dan membuat plot deret waktu untuk membandingkan frekuensi tweet.

Objek deret waktu untuk Puma dan Nike telah dimuat sebelumnya masing-masing sebagai puma_ts dan nike_ts.

Pustaka rtweet, reshape, dan ggplot2 juga telah dimuat sebelumnya.

Latihan ini adalah bagian dari kursus

Menganalisis Data Media Sosial dengan R

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

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

Edit dan Jalankan Kode

Latihan ini adalah bagian dari kursus

Menganalisis Data Media Sosial dengan R

SkillTag.level.intermediateSkillTag.label

4.9+

Mulai Kursus 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: Memfilter tweet Exercise 2: Menyaring tweet orisinal Exercise 3: Memfilter berdasarkan bahasa tweet Exercise 4: Filter berdasarkan popularitas tweet Exercise 5: Analisis pengguna Twitter Exercise 6: Ekstrak informasi pengguna Exercise 7: Jelajahi pengguna berdasarkan golden ratio Exercise 8: Pelanggan daftar twitter Exercise 9: Tren Twitter Exercise 10: Tren yang tersedia Exercise 11: Tren berdasarkan nama negara Exercise 12: Tren per kota dan tren yang paling banyak di-tweet Exercise 13: Memplot data Twitter dari waktu ke waktu Exercise 14: Memvisualisasikan frekuensi tweet Exercise 15: Membuat objek deret waktu Exercise 16: Bandingkan frekuensi tweet untuk dua merek

Latihan Saat Ini

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