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

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python jupyter-notebook natural-language-processing stocks sentiment-analysis news-headlines extract-stock-sentiment investment-insight financial-news data-scraping

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

Extract-Stock-Sentiment-From-News-Headlines

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

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, we generate investing insight by applying sentiment analysis on financial news headlines from Finviz. Using this natural language processing technique, we get to interpret the emotion behind the headlines and predict whether the market feels good or bad about a stock.

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