Skip to content

Latest commit

 

History

History
8 lines (5 loc) · 649 Bytes

File metadata and controls

8 lines (5 loc) · 649 Bytes

Extract-Stock-Sentiment-from-News-Headlines

Language Used: Python

In this project, it will generate investing insight by applying sentiment analysis on financial news headlines from Finviz. Using natural language processing technique it will understand the emotion behind the headlines and predict whether the market feels good or bad about a stock.

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.

Special Thanks to Juan González-Vallinas (Data Scientist at ABN AMRO) for guidance.