abraham3k 1.2.3

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

Basic Usage

You can run one command to do everything

from prophets import Isaiah

darthvader = Isaiah(news_source="google") 

scores = darthvader.sentiment(["amd", 
                               "microsoft", 
                               "tesla", 
                               "theranos"], 
                               window=2)
print(scores)

'''
{'amd': {'avg': 0.32922767,
         'desc_avg': 0.40470959,
         'info': {'news_source': 'google',
                  'splitting': False,
                  'weights': {'desc': 0.1, 'text': 0.8, 'title': 0.1}},
         'nice': 'positive',
         'text_avg': 0.31924348,
         'title_avg': 0.3336193},
 'microsoft': {'avg': 0.22709808,
               'desc_avg': 0.35126282,
               'info': {'news_source': 'google',
                        'splitting': False,
                        'weights': {'desc': 0.1, 'text': 0.8, 'title': 0.1}},
               'nice': 'positive',
               'text_avg': 0.22539444,
               'title_avg': 0.1165625},
 'tesla': {'avg': -0.20538455,
           'desc_avg': -0.22413444,
           'info': {'news_source': 'google',
                    'splitting': False,
                    'weights': {'desc': 0.1, 'text': 0.8, 'title': 0.1}},
           'nice': 'negative',
           'text_avg': -0.19356265,
           'title_avg': -0.28120986},
 'theranos': {'avg': -0.036198,
              'desc_avg': 0.03842,
              'info': {'news_source': 'google',
                       'splitting': False,
                       'weights': {'desc': 0.1, 'text': 0.8, 'title': 0.1}},
              'nice': 'neutral',
              'text_avg': -0.08745,
              'title_avg': 0.2992}}
'''

Changing News Sources

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. 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 to get your API key.

Detailed Usage

Currently, there are a couple extra options you can use to tweak the output.

When instatiating the class, you can pass up to five optional keyword arguments: news_source and api_key (as explained above), splitting, and weights.

• loud: bool - Whether or not the classifier prints out each individual average or not. Default: False.
• splitting: bool - Recursively splits a large text into sentences and analyzes each sentence individually, rather than examining the article as a block. Default: False.
• weights: dict - This chooses what each individual category (text, title, desc) is weighted as (must add up to 1). Default: weights={"title": 0.1, "desc": 0.1, "text": 0.8}.

When running the main function, sentiment, there is one requred argument, topics, and two optional keyword arguments: window and up_to.

• topics: list - The list of the topics (each a str) to search for.
• up_to: str - The latest day to search for, in format YYYY-MM-DD. Default: current date.
• window: int - How many days back from up_to to search for. Default 2.

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.