abraham3k 1.2.2

Algorithmically predict public sentiment on a topic using VADER sentiment analysis

abraham

Algorithmically predict public sentiment on a topic using VADER sentiment analysis.

Installation

Installation is simple; just install via pip.

$ pip3 install abraham3k

Sample Output

You can run one command to do everything -

from prophets import Isaiah

# splitting means that it recursively splits a large text into sentences and analyzes each individually
darthvader = Isaiah(news_source="google", splitting=True) 

# this command takes a bit of time to run because it has to download lots of articles
scores = darthvader.sentiment(["robinhood", 
                      "johnson and johnson", 
                      "bitcoin", 
                      "dogecoin", 
                      "biden",  
                      "amazon"], 
                      window=2, # how many days back from up_to to get news from
                      up_to="04/18/2021") # latest date to get news from

print(scores)

'''
{'robinhood': 
    {
        'avg': 0.3798676562301132, 
        'nice': 'positive :)'
     },
 'johnson and johnson': 
    {
        'avg': 0.27466788299009787, 
        'nice': 'positive :)'
    },
 'bitcoin': 
    {
        'avg': 0.28669931035859125, 
        'nice': 'positive :)'
    },
 'dogecoin': 
    {
        'avg': 0.2837840361036227, 
        'nice': 'positive :)'
    },
 'biden': 
    {
        'avg': 0.2404157345348728, 
        'nice': 'positive :)'
    },
 'amazon': 
    {
        'avg': 0.2894022880254384, 
        'nice': 'positive :)'
    }
}
'''

Or, you can run it step by step, as well.

from prophets import Isaiah

# splitting means that it recursively splits a large text into sentences and analyzes each individually
darthvader = Isaiah(news_source="google", splitting=True)

# this command takes a bit of time to run because it has to download lots of articles
articles = darthvader.get_articles(["robinhood", 
                      "johnson and johnson", 
                      "bitcoin", 
                      "dogecoin", 
                      "biden",  
                      "amazon"]
                      window=2, # how many days back from up_to to get news from
                      up_to="04/18/2021") # latest date to get news from

scores = darthvader.score_all(articles)

print(scores)

'''
{'robinhood': 
    {
        'avg': 0.3798676562301132, 
        'nice': 'positive :)'
     },
 'johnson and johnson': 
    {
        'avg': 0.27466788299009787, 
        'nice': 'positive :)'
    },
 'bitcoin': 
    {
        'avg': 0.28669931035859125, 
        'nice': 'positive :)'
    },
 'dogecoin': 
    {
        'avg': 0.2837840361036227, 
        'nice': 'positive :)'
    },
 'biden': 
    {
        'avg': 0.2404157345348728, 
        'nice': 'positive :)'
    },
 'amazon': 
    {
        'avg': 0.2894022880254384, 
        'nice': 'positive :)'
    }
}
'''

Isaiah supports two news sources: Google News and NewsAPI. Default is Google News, but you can change it to NewsAPI by passing Isaiah(news_source='newsapi', api_key='<your api key') when instantiating.

NewsAPI Integration

I'd highly recommend using NewsAPI. It's much better than the Google News API. Setup is really simple, just head to the register page and sign up. As I explained above, just pass your key to the constructor when instantiating.

Updates

I've made it pretty simple (at least for me) to push updates. Once I'm in the directory, I can run $ ./build-push 1.2.0 "update install requirements" where 1.2.0 is the version and "update install requirements" is the git commit message. It will update to PyPi and to the github repository.