Trump Sentiment Tracker

Introduction

The President of the United States, Donald Trump is arguably the most polarizing figure in the world today. No matter where your political allegiances lie, you likely have somewhat of a strong opinion on Trump. It seems we all do. Whether it’s the volatility of his actions or the brashness in the way he does what he does, it seems everyone is fixated on the topic of Trump. This has been reflected in tons of interesting projects such as Trump Tracker, FiveThirtyEights Popularity Ratings, and Track Trump. With these projects in mind, I thought I would throw my hat in the ring and ship something of my own.

 

Project

Click on the link below to head to the web application:

Trump Sentiment Tracker

 

Overview

I came into this project with several goals. First and foremost, I wanted to improve my development skills. Before this, I had never done any full-stack development so I was excited to take a project all the way from backend to frontend and finally deployment. Next, I wanted an opportunity to design something. One of the most challenging parts of creating Trump Sentiment Tracker was designing the user interface and figuring out how I wanted to communicate the insights most effectively.

Lastly, I wanted to build something that may be of interest to some people. While you can visit the aforementioned FiveThirtyEight link and see how Trump fares with the general population, I found myself more interested in a short-term approach. When dealing with this much volatility, I thought it would be interesting to see how the population is viewing the President every minute, every hour, etc.

 

Process

I created Trump Sentiment Tracker through a multi-step process that I’ll go ahead and outline below for the sake of clarity.

  1. Created a twitter streamer that collects tweets with ‘Trump’ and transfers them into a dataframe
  2. Utilized the VADER Sentiment Analysis tool in order to speculate how positive or negative each given tweet was
  3. Shortened the time frame to the last five minutes and averaged the sentiment scores for all the tweets
  4. Transferred the backend to a Flask application for deployment
  5. Planned out how I wanted to effectively convey insights and designed a user interface
  6. Put it all together and hosted the web app on Heroku

Reflection

Now that the project is shipped, I can look back and be very proud of what I was able to accomplish. Coming into this endeavor, I was admittedly quite inexperienced in full-stack development and web design. However, thanks to this project and all the steps I had to take in order to complete it, I can say that I am a much more competent and confident developer now.

Moving forward, I’m excited to see if Trump Sentiment Tracker gets any interest and most of all, I’m excited to start my next project.

 

Details

  • Languages: Python, Javascript, HTML, CSS
  • Environment: Flask, Pycharm
  • Date Completed: August 2017
  • Techniques Used: Full Stack Development, UI Design, Data Mining, Sentiment Analysis
  • See the Code: Github