finsent

Extract sentiment from financial news headlines for every US public company

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Finsent is a fast and seamless way to collect, classify and visualize sentiment polarity of financial news headlines for every US listed company.

Requirements:

• requests
• BeautifulSoup
• nltk
• Vader Corpus

Running:

The app uses a series of methods to perform different analysis and classification actions:

1. news_sent: A method that creates a DataFrame of all the headlines for a specific company and a classification of sentiment polarity. Just feed it a ticker and the df will be created:

#We define the ticker (Oracle Corp.):
ticker = 'ORCL'
#And call the method:
headlines_df = finsent(ticker).news_sent()
headlines_df

2. sentiment: A method that returns the aggregated sentiment of a given company:

#We define the ticker (Adobe Inc.):
ticker = 'ADBE'
#And call the method:
sentiment_df = finsent(ticker).sentiment()
sentiment_df

3. get_all_stocks: A method that returns a DataFrame with sentiment polarity scores for a given array of tickers:

#We define a list of tickers:
tickers = ['F','AMD','NVDA','MU','FB','AMZN']

#And call the method:
sentiment_companies = finsent.get_all_stocks(tickers)
sentiment_companies

Output:

The script took circa 2 seconds to capture and classify the sentiment embedded in over 600 key news headlines for a set of 6 companies, providing a convenient and time-efficient solution for a highly time-consuming practice.

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Enjoy!

License:

GNU General Public License v3.0

Copyright © 2019 giuetr