The tool first scrapes articles about user chosen stocks from major news outlets, then performs a sentiment analysis on them to determine if the article was positive or negative. Stocks are then given an averaged sentiment rating for all scraped articles and an overall "stock well-being score".
Sep - Dec 2020
I implemented this tool as a part of an Independent Study at Allegheny College during the Fall 2020 semester, using Python. The tool scrapes new articles from highly rated stock news websites thru Google News, analyzes them for sentiment (whether the text is positive, negative, or neutral) among other things, and then scores these articles to provide an overall rating of stock sentiment and well-being. The tool allows users to get a quick look into how a stock is being rated in the news and the public's general feelings on the stocks. Users can access the tool either using the command line interface or via a web interface that was implemented using Streamlit.
It takes a lot of time to read every available news article about a stock, whether you are a professional trader or an amateur. The tool quickly gathers all the relevant articles from highly rated stock websites in the user's defined date range, then analyzes their textual sentiments. The user can read the articles that were scraped if they choose or look at the numerous graphs on the tool depicting article sentiments and overall stock feelings/performance. This information can be utilized by the users when they are making stock buying and selling decisions.
Learn more about the tool by reading this report I wrote on it here.