chat-miner VS word_cloud

As a gift for my boyfriend, I used our WhatsApp chat to put together this visual and printed it on a poster (Tools used: mainly chat-miner, and Gimp for the final touch) Note: only the visual is OC, the GitHub project isn’t mine

As a gift for my boyfriend, I put together this visual and printed it on a poster (Tools used: mainly chat-miner, and Gimp for the final touch)

Project: https://github.com/joweich/chat-miner (Disclaimer: I'm maintaining this project) Tools: python, pandas, matplotlib, Photoshop Data: Native WhatsApp chat export function Method: Export chat logs, filter on the last 12 months, parse data via python and create sunbursts via a custom script based on matplotlib. Sunbursts are normalized by their respective maximum

The github tool they linked to can parse data from a variety of messaging apps, including whatsapp, telegram, instagram, signal and other. Every app has a different format for the chatlog but the github repo provides parsers for all of them.

Parsed data and created visuals with this tool

I put together this poster as a birthday gift for my boyfriend. We're in a long distance relationship, which is why frequent chatting is very important for us. I used this tool to create the charts from my WhatsApp chat

https://github.com/joweich/chat-miner it’s this

Thanks to u/joweich and his library, I was able to create wonderful chat visualizations.

Please see the readme in this github project for the tutorial on how to produce the single charts. This tool makes it as easy as it gets

wordcloud to make the word clouds

- Logo with WordCloud. The texts used were from the plot/synopsis from the Wikipedia pages of the top 20 anime according to MyAnimeList (excluding anime from the same series. I'm looking at you Gintama).

Here's a GitHub issue that tracks this.

Word arrangement generated using the wordcloud Python package with a custom placement mask (skull design by me) and custom color function. Common English words removed before processing.

After reading this tutorial you should now be able to generate your own Wordcloud using Python. Checkout the WordCloud for Python Documentation. Use your imagination and have fun. Please leave like or comment if you found this article interesting!

If you are proficient with Python then there is also the wordcloud library (and its homepage/documentation) to make a custom word cloud for yourself rather than relying on TagCrowd. For example, I noticed that using the HasteBin as a URL source caused TagCrowd to put some gibberish words in the cloud, like “donaeurtmt”, which do not appear when simply pasting the raw text into the text field.

The project is very heavily based on https://github.com/amueller/word_cloud

A quick and easy way to get some context into anything being mentioned ensure the CSV is only the responses and run it through a word cloud generator in Python to determine what those keywords are. That should help you see what your customers are at least mentioning and then go from there to look for surrounding context.