Twitter Data Mining In R

Part I

This first section assumes you have no knowledge in building a twitter app to be used for fetching data. You can skip directly to the second section here if you are able to build your own twitter app and get the required authentication keys.

Introduction:

Social media usage has grown rapidly over the past few decades. Most social networks we can think of now are so well established, making them a platform where people can not only talk to one another but also be able to come forward with different views and interests they would like to express. With an almost constant rate of increasing users each day, social networks such as Facebook and Twitter have become great sources of data which can be used in the broad field of Data Science:Talk of (those pretty annoying) targeted ads for example…

Gif: They just keep coming!!


With the help of APIs, we are easily able to get data from such platforms to be used for further analysis. In this article we will go through the first step of text mining in R using data fetched from Twitter. The main advantage of these APIs is that the data we will fetch comes in a well-structured format which makes our work easier when crunching. In this case we will use the readily available Twitter API and create our own Twitter app that will then help in fetching the data. The following steps will accomplish the task…


Creating a Twitter app

To create a twitter app we can use for fetching metadata, we first need to have a Twitter account. We then need to go to the twitter dev site https://developer.twitter.com/ and log in with our user account. On the top right corner should be a drop down menu next to your username, go to APPS. At this point if you are doing this for the first time your Apps section should be blank. Click on “Create an app” to… create an app.

create app

We then have to fill in the form below appropriately. Here is a breakdown of what’s required:

  • Name: Give your app a unique name of your choice, e.g UniqueName
  • Description: This can always be changed later, use this to provide a brief note on what your app is all about to be able to distinguish it from other apps you might create in future.
  • Website: This should be your application’s home page web site. It is however not applicable for most personal apps. Anything goes here e.g https://carlvinjerry.github.io
  • Callback URL: I would ignore the Callback URL field. If you are allowing users to log into your app to authenticate themselves, you’d enter the URL where they would be returned after they’ve given permission to Twitter to use your app.
App details

App details


The remaining fields should be quite straight forward but must be filled. Click “Create” once done and there you have your first twitter app. On your app is a menu with Keys and tokens. These are the most important components since we will need them to access data from the APIs.Generate both consumer and access tokens (if not readily available) and take note of them. NB: These keys are meant for your eyes only!

Keys


The final bit of setting up our twitter app is granting access permissions. We will mostly do fine with the read-only if all we need is to fetch data but it can always be changed later.

Permisions



Now we can move on to the next step where we set up R to query data from Twitter.





Part II

Setting up R to fetch twitter data

With our twitter app set up in part I above and we are able to get the authentication keys for the API, we can now easily fetch data from twitter in R. The following steps will help us do this:

Prerequisites:

  • Twitter API Keys: At this point we already have our twitter app with the required API keys.
  • R and an IDE of choice: We also need to have R installed, advisably the latest version. Microsoft’s enhanced R distribution is recommended over the base R but for this specific task either can do just fine. I would recommend R STUDIO for an IDE. One obvious advantage of all these is that they’re open-source tools.


1.Install and Load the required packages in R

R has a standard set of packages, each with different tasks. You can find some packages for download
here. The code chunk below installs and loads the specific packages we need for this task. Take note of comments at each line of code, initiated by an octothorp.

#Install packages
install.packages("twitteR")#------Extracts data from twitter
install.packages("httr")#--------Tools for Working with URLs and HTTP
#We can now load the two packages 
library("twitteR")
require("httr")#-------------Both require() and library() can be used to call an installed package


NB: Windows users might need to download a certification file and store it in the working directory. This certificate file initiates a handshake between R and the Twitter API.

# Download "cacert.pem" file
download.file(url="https://curl.haxx.se/ca/cacert.pem",destfile="cacert.pem")



2.Create and store objects containing the twitter authenticated credentials

This is where we invoke the twitter API using the credentials from our app and query the data we need.

# Authentication keys
consumer_key <- 'hjksdha08097afnjhaa90uaf'
consumer_secret <- 'hjksdha08097afnjhaa90uaf'
access_token <- 'hjksdha08097afnjhaa90uaf'
access_secret <- 'hjksdha08097afnjhaa90uaf'

#The above tokens are what we made the twitter app for.



3.Query data from twitter

We can now go ahead and fetch our data. Due to limitations on the twitter standard apps, it is advisable to store your data in R locally. This will reduce the number of times you have to make requests to twitter to fetch data. You will therefore do much more with your app that way regardless of the limitations- Or you can as well buy the premium app. In my example below, I am fetching data for a user on twitter called @UKenyatta.

#Connect to Twitter 
setup_twitter_oauth(consumer_key, consumer_secret, access_token, access_secret)
tweets <- userTimeline("UKenyatta", n=3200) #Standard twitter apps are limited to 3200 tweets per                                                  #download session. This could come out less depending on
                                            #the app

#create a data frame of the tweets
UKenyatta_Tweets <- tbl_df(map_df(tweets, as.data.frame))

# Save tweets for later (and note when saved):
save(UKenyatta_Tweets, file="UKenyatta_Tweets.RData")

# You can then access them later at will...
# load("UKenyatta_Tweets.RData")

You can now manipulate your data and see what you find out…



Avatar
Carlvin J Mwange
Quantitative Analyst

My research interests include data analysis & visualization, Machine Learning, AI and Financial Modeling.

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