Metadata-Version: 2.1
Name: abraham3k
Version: 1.3.4
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](https://img.shields.io/pypi/v/abraham3k)
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        Algorithmically predict public sentiment on a topic using flair sentiment analysis.
        
        ## Installation
        
        Installation is simple; just install via pip.
        
        ```bash
        $ pip3 install abraham3k
        ```
        
        ## Basic Usage
        
        You can run the main function, `news_sentiment` to get the raw scores. This will return a nested dictionary with keys for each topic.
        
        ```python
        from abraham3k.prophets import Isaiah
        
        darthvader = Isaiah(news_source="google") 
        
        scores = darthvader.news_sentiment(["amd", 
                                       "microsoft", 
                                       "tesla", 
                                       "theranos"], 
                                       window=2)
        print(scores['tesla']['text'])
        
        '''
                                                         desc              datetime  probability sentiment
        0   The latest PassMark ranking show AMD Intel swi...  2021-04-22T18:45:03Z     0.999276  NEGATIVE
        1   The X570 chipset AMD offer advanced feature se...  2021-04-22T14:33:07Z     0.999649  POSITIVE
        2   Apple released first developer beta macOS 11.4...  2021-04-21T19:10:02Z     0.990774  POSITIVE
        3   Prepare terror PC. The release highly anticipa...  2021-04-22T18:00:02Z     0.839055  POSITIVE
        4   Stressing ex x86 Canadian AI chip startup Tens...  2021-04-22T13:00:07Z     0.759295  POSITIVE
        ..                                                ...                   ...          ...       ...
        95  Orthopaedic Medical Group Tampa Bay (OMG) exci...  2021-04-21T22:46:00Z     0.979155  POSITIVE
        96  OtterBox appointed Leader, proudly 100% Austra...  2021-04-21T23:00:00Z     0.992927  POSITIVE
        97  WATG, world's leading global destination hospi...  2021-04-21T22:52:00Z     0.993889  POSITIVE
        98  AINQA Health Pte. Ltd. (Headquartered Singapor...  2021-04-22T02:30:00Z     0.641172  POSITIVE
        99  Press Release Nokia publish first-quarter repo...  2021-04-22T05:00:00Z     0.894449  NEGATIVE
        '''
        ```
        
        ## Changing News Sources
        
        `Isaiah` supports two news sources: [Google News](https://news.google.com/) and [NewsAPI](https://newsapi.org/). Default is [Google News](https://news.google.com/), but you can change it to [NewsAPI](https://newsapi.org/) by passing `Isaiah(news_source='newsapi', api_key='<your api key')` when instantiating. I'd highly recommend using [NewsAPI](https://newsapi.org/). It's much better than the [Google News](https://news.google.com/) API. Setup is really simple, just head to the [register](https://newsapi.org/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 ISO format (`%Y-%m-%dT%H:%M:%SZ`). 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!
        
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
