Metadata-Version: 2.1
Name: abraham3k
Version: 1.1.3
Summary: Algorithmically predict public sentiment on a topic using VADER sentiment analysis
Home-page: https://github.com/ckinateder/abraham
Author: Calvin Kinateder
Author-email: calvinkinateder@gmail.com
License: UNKNOWN
Project-URL: Bug Tracker, https://github.com/ckinateder/abraham/issues
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown

# abraham

[![PyPI version](https://badge.fury.io/py/abraham3k.svg)](https://badge.fury.io/py/abraham3k)

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

## Installation

Installation is simple; just install via pip.

```bash
$ pip3 install abraham3k
```

## Sample Output

You can run one command to do everything -

```python
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.

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


