📝 Documentation next steps: checklist

[jbesomi]

This document attempt at giving an order of importance of tasks related to improving the documentation of the website.

• Add the "Edit on Github" button in every Texthero web-page

Getting started

Expected documents:

1. Getting Started
2. Preprocessing (see Getting started: preprocessing #144)
3. Representation
4. NLP
5. Visualization
6. HeroSeries

• Write the "Preprocessing" page under Getting Started
• Write the "Representation" page under Getting Started
• Write the "NLP" page under Getting Started
• Write the "Visualization" page under Getting Started
• Write the "Kind of Series"/"HeroSeries" under Getting Started Getting started: Kind of Series (HeroSeries) #135

Aim

The documentation is probably one of the most important part of the whole project. We want to explain clearly to new users what Texthero is about and let them start using it quickly and effortlessly.

For example, reading the "getting started" should be a smooth and simple lecture. The different doc pages should be linked together and the "getting started" should appear as a story.

Also, it's important to have a unified view: the language and the structure of each page should be clear. Different pages should still share a similar structure.

Each page needs to have a clear and specific goal, clear both to the writer and also shared with the public. The first sentence of the page document should mention that. "The aim of this page is to explain ..."

See the discussion here for some extra comments: #135

Useful resources / Inspiration

• jalajthanaki/NLPython
• Tutorial: Keith Galli - Natural Language Processing (NLP) in Python - From Zero to Hero

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This document is a WIP

[mk2510]

@jbesomi, as we now have implemented the API-checklist, we will now focus more on the documentation part. There we (@henrifroese and I) will start writing, the visualisation, representation, nlp tutorial and also a developer documentation
We will link the pull request later on here. 🥇

[jbesomi]

Perfect!

For the topic modeling tutorial, something similar to this one might be a good start: discovering-hidden-topics-python