Cady Baltz, Bryant Hou, Kendra Huang
A program to help amateur traders make decisions by analyzing twitter headlines from popular news companies as well as various high-influence industry leaders and politicians' tweets.
We utilized Google Natural Language to determine tweet sentiment from 8 major sources: the New York Times, CNN Breaking News, the Economist, Guardian News, the Washington Post, Donald Trump (President of the United States), Joe Weisenthal (co-host, BloombergTV), and Vitalik Buterin (Etherium founder). We then designed an algorithm to create a score from the sentiment values, defining the score for buying, selling, and holding. The program is presented as a website using the React framework.
Frontend: React, HTML, CSS, JavaScript
Backend: Python, Google Natural Language Processing