Analyzing over a thousand tweets a minute to find out how President Donald Trump is faring in real-time Introduction The President of the United States, . No matter where your political allegiances lie, you likely have atleast a somewhat strong opinion on Trump. Whether it’s the volatility of his actions or the brashness of the way he does what he does, it seems everyone is fixated on him. Donald Trump is arguably the most polarizing figure in the world today This has been reflected in tons of interesting projects such as , , and . With these projects in mind, Trump Tracker FiveThirtyEights Popularity Ratings Track Trump I thought I would throw my hat in the ring and ship something of my own. Overview The web application streams all tweets containing the word ‘Trump’ within the last five minutes of being launched. The tweets are then put through sentiment analysis in order to determine how positive or negative they are. Lastly, the sentiment scores are averaged and reported to you using an intuitive visualization. The Trump Sentiment Tracker uses real-time twitter data to determine the current public perception of President Donald Trump. Not doing so hot right now… Outline The full process is outlined below and explained in more detail throughout the rest of the article: Created a that collects tweets with the word ‘Trump’ and transfers them into a dataframe twitter streamer Utilized the package in order to speculate how positive or negative each given tweet was VADER Sentiment Analysis Adjusted the time frame to the last five minutes and averaged the sentiment scores for all the tweets Transferred the back-end to a application for deployment Flask Planned out how I wanted to effectively convey insights and designed a user interface Put it all together and hosted the web app on Heroku See the Project If you aren’t interested in any of the behind the scenes stuff, no worries. Feel free to skip all the detail below and check out the code on my Github or the work itself at this link below: Trump Sentiment Tracker Goals I came into this project wanting to accomplish several things. . Prior to this, I had done back-end and front-end programming separately but . On top of this, I was also eager to take the opportunity to design something. I’ve recently developed an interest in design and I had never developed a web application user interface before diving into this project. First and foremost, I wanted to improve my full-stack development related skill sets I had never taken a project the full distance Tools and Software Coming into this project with little development background with , my first task was to decide on an environment. After a bit of consideration, I ended up going with (Version 2017.1.5). I found Pycharm to be very intuitive and effective for web application development. I’m sure I’ll be going back to it sometime in the near future. Python Pycharm If you are a student and are interested in Pycharm, you can get it for free here . I also used Python 3.5.2 and several other dependencies that you can find specified in the Github repository under the file. Lastly, I used Heroku to host the web application. requirements.txt Quick look at the Pycharm environment and workflow Back-End The first step of the project was to figure out a way to stream tweets and then format them properly for analysis. For the streaming, I choose to use and implemented a basic listener that while being run would collect every tweet with the word ‘Trump’ in it. The class then takes the current tweet and formats it properly to be added to the ongoing DataFrame. Tweepy pandas The DataFrame is constantly updated to only keep data within a certain time threshold (5 minutes) in order to get virtually real-time insights. After taking in data and formatting it correctly, sentiment analysis is performed. Following a bit of research, I decided to go with (Valence Aware Dictionary and Sentiment Reasoner). VADER I choose VADER because of it’s aptitude for social media data specifically. Front-End As far as the user interface goes, it was created using and HTML primarily. The meter was developed using Matt Magoffin’s open source as a jumping off point and the general feel of the website was in large part motivated by . D3.js project Track Trump One of the things that I struggled with the most was integrating the back-end with the front-end so that the meter would correspond with the current mean sentiment score. Eventually, I was able to work around this problem by combining different JavaScript files and using getters and setters to my advantage. As far as the user interface goes, you can see the transformation below: Deployment Once the project was working locally, the next step was to figure out how I wanted to deploy it. I considered a few different alternatives but ended up going with due to the abundance of great tutorials out there. I found the following video especially helpful, if any of you plan on checking out heroku anytime soon, I highly recommend it. Heroku That, and the fact that it’s free. Learning Resources As you can probably tell, I came into this journey needing to acquire some skills that weren’t quite where I wanted them to be. However, there were two that really stood out to me in the form of the free course by Udacity and various Youtube tutorials by . In order to do this, I used a seemingly infinite amount of online resources. Full Stack Development sentdex Full Stack Foundations | Udacity sentdex Reflection Now that the project is shipped, I can look back and be very proud of what I was able to accomplish. There was times throughout the learning process where I undoubtedly struggled and thought about quitting but I’m glad that I stuck it out. By sticking with it and staying patient, I ended up acquiring some skills and techniques that I wouldn’t have right now otherwise. 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. Wrapping Things Up If you’re interested in the code, be sure to check it out on . If you have any feedback or inquiries, feel free to reach out to me on and . You can also see more of my work on . I hope you enjoyed hearing about the making of Trump Sentiment Tracker . Github Twitter LinkedIn my website Lastly, be sure to subscribe to my weekly data science newsletter below. Thanks for reading! Originally published at conordewey.com on August 31, 2017.

How I Built Trump Sentiment Tracker

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