abraham3k 1.1.1

Algorithmically predict public sentiment on a topic using VADER sentiment analysis

abraham

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

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](google news) and NewsAPI. Default is [Google News](google news), but you can change it to NewsAPI by passing Isaiah(news_source='newsapi') when instantiating. In order to use NewsAPI, you have to put your api key in keys/newsapi_org.