semvec/pypi-packages huggingface dataset:
wagtailtextanalysis 0.0.1
Detect key phrases and sentiment from your Wagtail content using cloud Api services
License: MIT

[![Build Status](https://travis-ci.org/marteinn/wagtail-text-analysis.svg?branch=develop)](https://travis-ci.org/marteinn/wagtail-text-analysis)

# Wagtail Text Analysis

Detect key phrases from your Wagtail content using powerful ML Apis.


## Features

- Key phrase generation using Azure Text Analytics
- Text sentiment using Azure Text Analytics


## Supported providers

- [Azure Text Analytics](#azure-text-analytics)


## Requirements

- Python 3.6+
- Wagtail 2+
- Access to any of the [supported providers](#providers)


## Providers

### Azure Text Analytics

Azure's Text Analytics Api. [Docs](https://azure.microsoft.com/en-us/services/cognitive-services/text-analytics/)


#### Settings

- `AZURE_TEXT_ANALYTICS_API_KEY`: Azure Computer Vision API key
- `AZURE_TEXT_ANALYTICS_REGION`: The default region to use, e.g. westus, northeurope, etc


## Where to go from here?

We recommend you to check out our [Getting Started Guide](https://github.com/marteinn/wagtail-text-analysis/blob/develop/docs/getting-started.md).


## Contributing

Want to contribute? Awesome. Just send a pull request.


## License

Wagtail Text Analysis is released under the [MIT License](http://www.opensource.org/licenses/MIT).
spacy-symspell 0.1.2
spaCy pipeline component for spelling correction using sysmepll.
License: MIT
Requires Python: >=3.5

# spaCy Symspell
## Spelling correction implementation in spaCy via Symspell

This package is a [spaCy 2.0 extension](https://spacy.io/usage/processing-pipelines#section-extensions) that adds sentnece/spelling corrections via Symspell to spaCy's text processing pipeline.

## Installation

`pip install spacy_symspell`

## Notes
This package is still in Alpha and there may be unforeseen errors. Dictionary loading time is also significant, can take up to 30 seconds on slow machines.

## Usage

Adding the component to the processing pipeline is relatively simple:

    import spacy
    from spacy_symspell import SpellingCorrector

    nlp = spacy.load('en_core_web_sm')
    corrector = SpellingCorrector()
    nlp.add_pipe(corrector)
    doc = nlp('What doyuoknowabout antyhing')

    for s in doc._.suggestions:#iterable
        print(s) #What doyon about anything

    doc._.segmentation  #::segmented_string - What doyouk now about antyhing ::corrected_string - that dook now about anything

spaCy_symspell operates on `Doc` and `Span` spaCy objects. When called on a `Doc` or `Span`, the object is given two attributes: `suggestions` (a list of all found spelling suggestions) and `segmentation` (a corrected sentence in the case of ommitted spaces).

## Todo
Symspell accuracy can be improved with the help of spaCy by extracting and analyzing resulting n-grams and cross-referencing with possible n-grams deductible from the character groups in the symspell result. For example the correction 'that dook now' leaves us with a verbless sentence, and on closer analysis will reveal that the character group 'now' is related with the verb 'know', and the verb know is associated with the n-gram 'you know'.

## Under the hood
[spacy_symspell](https://github.com/xwiz/spacy_symspell) is currently a wrapper of the [python port](https://github.com/mammothb/symspellpy) for [Symspell](https://github.com/wolfgarbe/SymSpell). For additional details, see the linked project pages.