abraham3k 1.2.7

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 execute the main function news_sentiment_summary.

from abraham3k.prophets import Isaiah

darthvader = Isaiah(news_source="google") 

scores = darthvader.news_sentiment_summary(["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}}
'''

You can also run a separate function, summary to get the raw scores. This will return a nested dictionary with keys for each topic.

from abraham3k.prophets import Isaiah

darthvader = Isaiah(news_source="google") 

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

'''
[87 rows x 6 columns]
      neg    neu    pos  compound                                           sentence              datetime
0   0.200  0.800  0.000   -0.7184  This weekend two men aged 59 69 died Tesla Mod...  2021-04-20T01:12:33Z
1   0.113  0.714  0.172    0.1027  The National Highway Transportation Safety Adm...  2021-04-19T17:11:20Z
2   0.211  0.789  0.000   -0.5859  Tesla working vehicle tailored Chinese consume...  2021-04-20T09:31:36Z
3   0.000  0.702  0.298    0.7003  Amazon told Bloomberg thatit canned Lord Rings...  2021-04-19T11:30:30Z
4   0.128  0.769  0.103   -0.1779  The first fatal incident involve Tesla one dri...  2021-04-19T15:42:47Z
..    ...    ...    ...       ...                                                ...                   ...
76  0.349  0.509  0.142   -0.7717  Two people killed fiery crash Tesla authority ...  2021-04-19T04:02:56Z
77  0.094  0.906  0.000   -0.3818  LiveUpdated April 20, 2021, 11:51 a.m. ET Apri...  2021-04-20T15:36:21Z
78  0.087  0.837  0.076   -0.0754  Though SpaceX‘s new Starlink satellite current...  2021-04-19T08:25:20Z
79  0.275  0.725  0.000   -0.8225  Over weekend, Tesla Model S involved accident ...  2021-04-20T09:50:12Z
80  0.332  0.514  0.154   -0.7096  My heart sink I write AI going wrong. Behind e...  2021-04-20T10:39:02Z
'''

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 functions, news_sentiment and news_sentiment_summary, 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.

Notes

Currently, there's another algorithm in progress (SALT), including salt.py and salt.ipynb in the abraham3k/ directory and the entire models/ directory. They're not ready for use yet, so don't worry about importing them or anything.

Contributions

Pull requests welcome!