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Scrape news headlines for FB and TSLA then apply sentiment analysis to generate investment insight.

Home Page: https://satyam9090.github.io/Extract-Stock-Sentiment-from-News-Headlines/

Python 1.64% HTML 95.62% Jupyter Notebook 2.74%
news-headlines extract-stock-sentiment finviz sentiment-analysis visualization tsla fb stock-sentiment machine-learning artificial-intelligence ai ml jupyter-notebook python python3 finance-application

extract-stock-sentiment-from-news-headlines's Introduction

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.

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