Collecting / Storing Tweets with Python and MongoDB

A good amount of the work that I do involves using social media content for analyzing networks, sentiment, influencers and other various types of analysis.

In order to do this type of analysis, you first need to have some data to analyze.  You can also scrape websites like Twitter or Facebook using simple web scrapers, but I’ve always found it easier to use the API’s that these companies / websites provide to pull down data.

The Twitter Streaming API is ideal for grabbing data in real-time and storing it for analysis. Twitter also has a search API that lets you pull down a certain number of historical tweets (I think I read it was the last 1,000 tweets…but its been a while since I’ve looked at the Search API).   I’m a fan of the Streaming API because it lets me grab a much larger set of data than the Search API, but it requires you to build a script that ‘listens’ to the API for your required keywords and then store those tweets somewhere for later analysis.

There are tons of ways to connect up to the Streaming API. There are also quite a few Twitter API wrappers for Python (and most of them work very well).   I tend to use Tweepy more than others due to its ease of use and simple structure. Additionally, if I’m working on a small / short-term project, I tend to reach for MongoDB to store the tweets using the PyMongo module. For larger / longer-term projects I usually connect the streaming API script to MySQL instead of MongoDB simply because MySQL fits into my ecosystem of backup scripts, etc better than MongoDB does.  MongoDB is perfectly suited for this type of work for larger projects…I just tend to swing toward MySQL for those projects.

For this post, I wanted to share my script for collecting Tweets from the Twitter API and storing them into MongoDB.

Note: This script is a mashup of many other scripts I’ve found on the web over the years. I don’t recall where I found the pieces/parts of this script but I don’t want to discount the help I had from other people / sites in building this script.

Collecting / Storing Tweets with Python and MongoDB

Let’s set up our imports:

Next, set up your mongoDB path:

Next, set up the words that you want to ‘listen’ for on Twitter. You can use words or phrases seperated by commas.

Here, I’m listening for words related to maching learning, data science, etc.

Next, let’s set up our Twitter API Access information.  You can set these up here.

Time to build the listener class.

Now that we have the listener class, let’s set everything up to start listening.

Now you are ready to go. The full script is below. You can store this script as “streaming_API.py” and run it as “python streaming_API.py” and – assuming you set up mongoDB and your twitter API key’s correctly, you should start collecting Tweets.

The Full Script:

 

Jupyter with Vagrant

I’ve written about using vagrant for 99.9% of my python work on here before (see here and here for examples).   In addition to vagrant, I use jupyter notebooks on 99.9% of the work that I do, so I figured I’d spend a little time describing how I use jupyter with vagrant.

First off, you’ll need to have vagrant set up and running (descriptions for linux, MacOS, Windows).   Once you have vagrant installed, we need to make a few changes to the VagrantFile to allow port forwarding from the vagrant virtual machine to the browser on your computer. If you followed the Vagrant on Windows post, you’ll have already set up the configuration that you need for vagrant to forward the necessary port for jupyter.   For those that haven’t read that post, below are the tweaks you need to make.

My default VagrantFile is shown in figure 1 below.

VagrantFile Example
Figure 1: VagrantFile Example

You’ll only need to change 1 line to get port forwarding working.   You’ll need to change the line that reads:

to the following:

This line will forward port 8888 on the guest to port 8888 on the host. If you aren’t using the default port of 8888 for jupyter, you’ll need to change ‘8888’ to the port you wish to use.

Now that the VagrantFile is ready to go, do a quick ‘vagrant up’ and ‘vagrant ssh’ to start your vagrant VM and log into it. Next, set up any virtual environments that you want / need (I use virtualenv to set up a virtual environment for every project).  You can skip this step if you wish, but it is recommended.

If you set up a virtual environment, go ahead and source into it so that you are using a clean environment and then run the command below to install jupyter. If you didn’t go then you can just run the below to install jupyter.

You are all set.  Jupyter should be installed and ready to go. To run it so it is accessible from your browser, just run the following command:

This command tells jupyter to listen on any IP address.

In your browser,  you should be able to visit your new fangled jupyter (via vagrant) instance by visiting the following url:

Now you’re ready to go with jupyter with vagrant.


Note: If you are wanting / needing to learn Jupyter, I highly recommend Learning IPython for Interactive Computing and Data Visualization (amazon affiliate link). I recommend it to all my clients who are just getting started with jupyter and ipython.

 


 

Vagrant on Windows

There are many different ways to install python and work with python on Windows. You can install Canopy or Anaconda to have an entire python ecosystem self-contained or you can install python directly onto your machine and configure all the bits and bytes yourself. My current recommendation is to use Vagrant on Windows combined with Virtualbox to virtualize your development environment.

While I use a mac or the majority of my development, I do find myself using Windows 10 more and more, and may be moving to a Windows machine in the future for my day-to-day laptop.  I have and do use Canopy and/or Anaconda but I’ve recently moved the majority of my python development on Windows into Linux (Ubuntu) virtual machines using Vagrant and Virtualbox. You can use other products like VMWare’s virtual machine platform, but Virtualbox is free and does a good enough job for day-to-day development.1

One Caveat: if you’re doing processor / memory intensive development with python, this may not be the best option for you. That said, it can work for those types of development efforts if you configure your virtual machine with enough RAM and processors.

To get started, you’ll need to download and install Vagrant and Virtualbox for your machine.   I am using Vagrant 1.90 and Virtualbox 5.1.10 at the time of this post.

Feel free to ‘run’ either of the programs, but there’s no need to enter either program just yet.    To really use the Vagrant and the linux virtual machine, you’ll need to download a *nix emulator to allow you to do the things you need to with vagrant.

I use Git’s “bash” to interface with my virtual machines and Vagrant.  You could use putty or CygWin or any other emulator, but I’ve found Git’s bash to be the easiest and simplest to install and use.  Jump over and download git for your machine and install it. At the time of writing, I’m using Git 2.11.0.

While installing Git, I recommend leaving everything checked on the ‘select’ components window if you don’t currently have any git applications installed. If you want to use other git applications, you can uncheck the “associate .git* configuration files…” option.  There is one ‘gotcha’ when installing git that you should be aware of.

On the “adjusting your path” section (see figure 1), you’ll need to think about how you want to use git on the command line.

git command line path
Figure 1: Adjusting your path

I selected the third option when installing git. I do not use git from the windows command line though…I use a git GUI along with git from the command line within my virtual environment.

Another screen to consider is the “Configuring the terminal emulator…” screen (figure 2).  I selected and use the MinTTY option because it gives me a much more *nix feel. This is personal preference. If you are going to be doing a lot of interactive python work in the console, you might want to select the 2nd option to use the windows default console window.

Configuring your terminal emulator
Figure 2: Configuring your terminal emulator

During the remainder of the installation, I left the rest of the options at the defaults.

Now that git (and bash) is installed, you can launch Git Bash to start working with Vagrant. You should see a window similar to Figure 3.

Git Bash
Figure 3: Git Bash

From this point, you can do your ‘vagrant init’, ‘vagrant up’ and ‘vagrant ssh’ to initialize, create and ssh into your vagrant machine.

Setting up Vagrant on Windows

For those of you that haven’t used Vagrant in the past, here’s how I set it up and use it. I generally use vagrant in this way to run jupyter, so I’ll walk you through setting things up for jupyter, pandas, etc.

First, set up a directory for your project. At the Bash command line, change into the directory you want to work from and type “mkdir vagrant_project” (or whatever name you want to use). Now, initialize your vagrant project by typing:

This will create a Vagrantfile in the directory you’re in. This will allow you to set the configuration of your virtual machine and Vagrant. The Vagrantfile should look something like this:

VagrantFile Example
Figure 4: VagrantFile Example

Before we go any further, open up your Vagrantfile and change the following line:

change “base” to “ubuntu/xenial64” to run Ubuntu 16.04. The line should now read:

If you want to run other flavors of linux or other OS’s, you can find others at https://atlas.hashicorp.com/search.

Since I’m setting this VM up to work with jupyter, I also want to configure port forwarding in the Vagrantfile. Look for the line that reads:

and add a line directly below that line to read:

This addition creates a forwarded port on your system from port 8888 on your host (your windows machine) to port 8888 on your guest (the virtual machine). This will allow you to access your jupyter notebooks from your Windows browser.

At this point, you could also configure lots of other vagrant options, but these are the bare minimums that you need to get started.

At the Bash command line, you can now type “vagrant up” to build your virtual machine. Since this is the first time you’ve run the command on this directory, it will go out and download the ubuntu/xenial64 ‘box’ and then build the virtual machine with the defaults.  You might see a Windows alert asking to ‘approve’ vagrant to make some changes…go ahead and allow that.

Once the ‘vagrant up’ command is complete, you should see something similar to Figure 5 below.

Output of Vagrant Up
Figure 5: Output of ‘vagrant up’

Now, you can ‘vagrant ssh’ to get into the virtual machine.  You should then see something similar to Figure 6. Now your running vagrant on windows!

Vagrant SSH - Vagrant on Windows
Figure 6: Output of ‘vagrant ssh’

One of the really cool things that vagrant does by default is set up shared folders. This allows you to do your development work in your favorite IDE or editor and have the changes show up automatically in your vagrant virtual machine.

At the Bash command line, type:

You should see a directory listing that has your Vagrantfile and a log file. If you visit your project directory using windows explorer, you should see the same two files. Shared folders for the win! I know its just a small thing, but it makes things easier for initial setup.

You now have vagrant on windows!

Configure the Python Environment

Time to set up your python environment.

First, install pip.

Even though you’ve set up a virtual machine for development, it is still a good idea to use virtualenv to separate multiple projects requirements.  Install install virtualenv  with the following command:

In your project directory, set up your virtual environment by typing:

Note: You may run unto an error while running this command. It will be something like like the message below:

If this happens, delete the ‘env’ folder and then add ‘–always-copy’ to the command and re-run it. See here for more details.

Activate your virtualenv by typing:

We’re ready to install pandas and jupyter using the command below. This will install both modules as well as their dependencies.

Now you’re ready to run jupyter.

In the above command, we start jupyter notebook with an extra config line of ‘–ip=0.0.0.0’. This tells jupyter to listen on any IP address. It may not always be necessary, but I find it cuts out a lot of issues when I’m running it in vagrant like this.

In your windows browser, visit ‘http://localhost:8888/tree’ and  – assuming everything went the way it should – you should see your jupyter notebook tree.

Jupyter via Vagrant VM
Figure 7: Jupyter via Vagrant VM

From here, you can create your notebooks and run them just like you would with any other platform.