DiscordWordCloud

A Python 3.9 discord bot that generates a word cloud (now with emojis !) for each discord user.

How to run it

Clone the project wherever you want
Install Python 3.9 from https://www.python.org/ (you can probably use an older version too)
Install the requirements listed in requirements.txt with pip
- use python -m pip install -r requirements.txt at the root of the project
- consult this guide if you need help with pip https://packaging.python.org/tutorials/installing-packages/
add a token.txt file at the root of the project
go to https://discordapp.com/developers/applications/ create your app
- add a User Bot to it and paste its Token in token.txt
- enable SERVER MEMBERS INTENT in the bot tab
- invite the bot with https://discord.com/api/oauth2/authorize?client_id=CLIENT_ID&permissions=0&scope=bot replace CLIENT_ID with the Client ID of your app
run main.py with Python 3.9

And it should run !

If you got any questions about this project, feel free to DM Inspi#8989 on Discord.

Features

;load (days) use this to load messages up to x days on your server
;cloud to generate your word cloud, you can also tag someone in the command to generate their word cloud
;emojis to get the custom emojis usage of the server

Pictures

How it works

Here's a step by step of how the bot makes a wordcloud:

after ;load
- for each word w and user u, compute p(w|u), the probability of u writing w (this data is separated between servers)
after ;cloud
- for each word w and the user u for which the cloud is, compute p(w|u)/p(w), this quantifies how much u favors w compared to everyone else
- uses the WordCloud lib to make a word cloud image, scaling the words in proportion to their scores (they are placed randomly)

Some notes regarding this model:

This model as-is would be filled with typos, bits of url and words that have only been written by u because p(w|u)/p(w) would be high; to counter this, a value α is added to every p(w|u) as if each user had at least an α probability of writing any word. This regularisation has the downside of putting some words that were never written by u in the word cloud.
A strong point of this model is that it works in any language and stop-words such as and, the, a, etc. are often excluded because they are not favored by anyone in particular.
One limitation of the model is that it does not handle expressions bigger than a single word or emoji.

The code is written so that implementing a new model is easy, by subclassing WCModel in wcmodel.py, and importing your custom class instead of WCBaseline in main.py !

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