Extract Stock Sentiment from News Headlines

Project Description

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](https://en.wikipedia.org/wiki/Sentiment_analysis) on financial news headlines from [Finviz](https://finviz.com). Using this [natural language processing](https://en.wikipedia.org/wiki/Natural_language_processing) technique, you 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](https://finviz.com/quote.ashx?t=FB) (FB) and [Tesla](https://finviz.com/quote.ashx?t=TSLA) (TSLA) stocks from [FINVIZ.com](https://finviz.com/), a popular website dedicated to stock information and news.

Project Tasks

1. 1
   Searching for gold inside HTML files
2. 2
   What is inside those files anyway?
3. 3
   Extra, extra! Extract the news headlines
4. 4
   Make NLTK think like a financial journalist
5. 5
   BREAKING NEWS: NLTK Crushes Sentiment Estimates
6. 6
   Plot all the sentiment in subplots
7. 7
   Weekends and duplicates
8. 8
   Sentiment on one single trading day and stock
9. 9
   Visualize the single day