Twitter Sentiment Analysis With Raspberry Pi





Introduction: Twitter Sentiment Analysis With Raspberry Pi

What is sentiment analysis, and why should you care about it?

Sentiment analysis is the process of determining the emotional tone behind a series of words, used to gain an understanding of the the attitudes, opinions and emotions expressed within an online mention. Sentiment analysis is extremely useful in social media monitoring as it allows us to gain an overview of the wider public opinion behind certain topics. The applications are broad and powerful. The ability to extract insights from social data is a practice that is being widely adopted by organisations across the world.Fun fact: The Obama administration used sentiment analysis to gauge public opinion to policy announcements and campaign messages ahead of 2012 presidential election.

Step 1: Wiring Up!

For this project you will need:

  • Raspberry Pi (in our case: Raspberry Pi 3 Model B)
  • 3 LED diodes (green, yellow and red) for representing the mood, calculated from the sentiment analysis
  • 3 resistors (in our case 330 Ohm) to protect your GPIO pins
  • wires, or a female cable (in our case 40 pin)

Now, you have to connect the led diodes on the specific GPIO pins on the Raspberry Pi (you can choose other pins, but you will have to refactor the code afterwards). Make sure you Raspberry Pi is turned off.
Then, connect the resistors on the anodes of the LED diodes. After that, you should connect your green diode on the pin 21, yellow on the pin 24 and the red on the pin 15. All of the cathodes should be connected to the Ground pins. Now you are all set to jump on the next step!

Step 2: Import the Packages

You'll need a couple of packages in order for the code to work.

  • Tweepy: python library for the official Twitter API. pip3 install tweepy
  • TextBlob: python library for processing textual data. pip3 install textblob
  • Pillow: python library for the user interface. pip3 install pillow

The following packages usually come bundled with python3, but in case you get compilation error, simply install them using the pip3 command:

  • Statistics: python library for statistics.
  • Matplotlib: python library for graphics representation of data.
  • Tkinter: python library for the user interface.
  • RPi.GPIO: python library that's available only on a RaspberryPi (but hey, we're doing this for a RasberryPi exclusively), that manages the GPIO pins.

NOTE: In order to test this on desktop: simply comment out 'import' in the script.

Step 3: Implementation

Place the following scripts together in a directory on the RaspberryPi:

  • - The entry point for the app. (run this script in the console).
  • - Script that connects to the Twitter API, processes the data and generates results.
  • - Script that generates a graphic representation of the results.
  • - Script that handles the diodes on the RaspberryPi.

Contributors: Zafir Stojanovski (151015) & Filip Spasovski (151049)




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    We have a be nice policy.
    Please be positive and constructive.




    That's an interesting way to track feedback :) Cool idea!

    Thanks a lot Swansong, glad you like it!

    This is a nice article on a subject not too many folks know about outside of marketing and public relations. Most of the folks experimenting with opinion mining just use a computer to display the results but running the libraries and Twitter API and adding the LED's was a fun idea.

    Using a breakout cable and providing a schematic or connection drawing would make it easier for others to see how to attach the LEDs if they are new to Pi hardware interfacing. A bit more detail on how to install and run the application would have been nice too.

    Really short but interesting.


    Hello NetZener,
    Thank you for your interesting feedback. We posted a little update that can be seen in the pictures above. We are beginners too, which means everyone can make this project. Unfortunately, we had a limited time for this project, and that's why it's a little bit short.

    Thanks again!