Inspiration - Our inspiration for developing this app derived from wanting to read political news while having the certainty that the information presented is strictly factual with little to no bias.
What it does - The application is an intuitive tool in which the user inputs the URL of an article of their choice, which is then scanned in an attempt to identify the magnitude and favorability of the author's sentiment. Through this identification process, the tool is able to provide the user an output determining whether the article they have input is one of five potential author sentiment outcomes: Impartial, Highly Favorable, Favorable Neutral, Highly Unfavorable, Unfavorable Neutral, and Mixed.
How I built it - The application was built by using Swift as our frontend interface and Python as our backend server. We implemented Google's Natural Language Processing API to gain access to their sentimentality Analysis. The backend includes the code in python that extracts the HTML code from the url, locates the article text in the HTML code, and sends it to the API for processing.
Challenges I ran into - As a team we ran into several challenges mainly towards the end when we began to combine all the elements of the application in an attempt to run a fluent and user-friendly process. Naturally, the exhaustion experienced towards the last day added to the challenge of finalizing a deliverable in time for the demonstration. Setting up the python server was a big issue since none of us had experience in creating one before. Another issue was that some of us were new to swift and python. All of us had to learn new technologies while developing our app.
Accomplishments that I'm proud of - We're all proud of working with each other in developing this project. We helped each other learn new technologies that were put into making this project and
What I learned - We learned how to communicate effectively with each other using software development techniques. We also gained a high-level understanding of Swift, front-end and backend integration, and other libraries and frameworks for both python and swift.
What's next for Debaits - Debait currently is an MVP, however, in the short time of working on the application we identified several components we would like to incorporate in the foreseeable future to enhance the application's efficiency and accuracy.
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