Extract Stock Sentiment from News Headlines

Note: this Project is soft launched, which means you may experience bugs. Please click "Report an Issue" in the top-right corner of the Project interface to provide feedback.

It used to take days for financial news to spread via radio, newspapers, and word of mouth. Now, in the age of the internet, it takes seconds. Did you know news articles are automatically being generated from figures and earnings call streams? In this Project, you will generate investing insight by applying sentiment analysis on financial news headlines from Finviz. Using this natural language processing technique, you will understand the emotion behind the headlines and predict whether the market feels good or bad about a stock.

This Project lets you apply the skills from Intermediate Python for Data Science, Manipulating DataFrames with pandas, and Natural Language Processing Fundamentals in Python. We recommend that you take those courses before starting this Project. Familiarity with the Beautiful Soup package may also be helpful.

The datasets used in this Project are raw HTML files for the Facebook (FB) and Tesla (TSLA) stocks from FINVIZ.com, a popular website dedicated to stock information and news.