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Curs

Analyzing Social Media Data in R

IntermediarNivel de competențe

Actualizat 02.2023

Extract and visualize Twitter data, perform sentiment and network analysis, and map the geolocation of your tweets.

Începe cursul gratuit

RData Manipulation

4 h

16 videoclipuri

57 Exerciții

4,700 XP

6,973

Certificat de realizare

Îndrăgit de cursanți din mii de companii

Formare pentru o echipă?

Încearcă pentru afaceri

Descrierea cursului

Analyzing data from social media can provide you with valuable insights. It can inform campaign strategies, improve marketing and sales, measure customer engagement, perform competitor analysis, and identify untapped networks. In this course, you’ll use R to extract and visualize Twitter data, perform network analysis, and view the geolocation of tweets. You’ll use a variety of datasets to put what you’ve learned into play, including tweets about celebrities, technology companies, trending topics, and sports.

Cerințe prealabile

Introduction to R

1

Understanding Twitter data

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.

Analyzing twitter data

Power of twitter data

Pros and cons of twitter data

Extracting twitter data

Prerequisites to set up the R environment

Search and extract tweets

Search and extract timelines

Components of twitter data

User interest and tweet counts

Compare follower count

Retweet counts

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2

Analyzing Twitter data

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.

Filtering tweets

Filtering for original tweets

Filtering on tweet language

Filter based on tweet popularity

Twitter user analysis

Extract user information

Explore users based on the golden ratio

Subscribers to twitter lists

Twitter trends

Available trends

Trends by country name

Trends by city and most tweeted trends

Plotting twitter data over time

Visualizing frequency of tweets

Create time series objects

Compare tweet frequencies for two brands

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3

Visualize Tweet texts

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.

Processing twitter text

Remove URLs and characters other than letters

Build a corpus and convert to lowercase

Remove stop words and additional spaces

Visualize popular terms

Removing custom stop words

Visualize popular terms with bar plots

Word clouds for visualization

Topic modeling of tweets

The LDA algorithm

Create a document term matrix

Create a topic model

Twitter sentiment analysis

Extract sentiment scores

Perform sentiment analysis

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4

Network Analysis and putting Twitter data on the map

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.

Twitter network analysis

Preparing data for a retweet network

Create a retweet network

Network centrality measures

Calculate out-degree scores

Compute the in-degree scores

Calculate the betweenness scores

Visualizing twitter networks

Create a network plot with attributes

Network plot based on centrality measure

Follower count to enhance the network plot

Putting twitter data on the map

Extract geolocation coordinates

Twitter data on the map

Course wrap-up

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Analyzing Social Media Data in R

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