Metadata-Version: 2.1
Name: abraham3k
Version: 1.1.4
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
Description: # 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`.
        
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
