Wessel Stoop

Machine learning, language, videogames


Welcome! My name is Wessel Stoop. I work with language & machine learning, and make videogames in my free time. This website aims to be a collection of the software and prose I write, and what the media subsequently write about these. Furthermore, this website contains some pictures of me to suggest that I totally do not spend all of my waking hours staring at a computer screen.




Olvand is a small multiplayer role playing game. It encourages players to build towns together, and contains various minigames. At its peak, it had an active fanbase of around a hundred people, running servers and creating secundary material like tutorials, wikis and Youtube videos.

Tic Tac Team

Tic Tac Team is a two-player puzzle game for the Apple iPad. It contains almost no language, which forces the two players to figure out the game mechanics together, and then create a small communication system to achieve their goals. Together with Jop van Heesch.

Vowel Space Travel

Vowel Space Travel is a simple tool that asks the player to identify differences between vowels in English, hoping that this will improve their language skills. My goal was to make an app around this repetitive task that makes it more attractive, in a futuristic setting.


Interactive explanations

I'm a big proponent of explaining complicated things with interactive visualizations: being able to 'play with an idea' is not only a much more efficient way to understand something, I believe it will also make the understanding much deeper.

Non-interactive texts

Game design

I've documented the development of my hobby game Olvand quite intensively. The blogs can be found at IndieDB. Some highlights:

Language technology


I've written a number of popular science writings about linguistics in Dutch, aimed students and researchers of linguistics. Highlights:

Software projects

You are what you tweet (github)

You are what you tweet is a webdemo displaying the power of language technology and machine learning: it imports all tweets of a particular Twitter timeline, and then performs text prediction and term profiling on it, as well as text classification. For that last task, it has language models for gender, age, aggression and sarcasm. Together with Florian Kunneman.

Stemming2017 (github)

Stemming 2017 is a webdemo displaying research by Eric Sanders on whether you can predict elections based on language analysis on Twitter. With over 10.000 visitors, this is my most popular project to date. Unfortunately, the predictions were further off than we'd hoped (but still a lot better than chance). Together with Eric Sanders.

Soothsayer (github)

Soothsayer is my master's thesis project about text prediction. In the thesis, I showed that text prediction improves when (1) using language models based on text written by the user and (2) that text written by friends of the user also improve the results. In the demo, you can test Soothsayer with various language models. The thesis led to various publications, media attention, and the Radboud University 2013 thesis prize. I explain my project in the video below:

Robot Nao visited the department where I work. As a fun sideproject, I wrote a game for it where it asks questions that should be answered with yes or no. With this information, it can guess which colleague you had in mind (apologies for the vertical video ;) ):

Procedural meshes & subdivision

Catmull-Clark subdivision is a smoothing algorithm and a basic tool in 3D modeling software. It was, however, not yet available for the Unity game engine. I created an implementation of it, which is available at the Unity Asset Store.

Fowlt (github)

Fowlt is the English version of Valkuil, a context-sensitive spelling corrector using machine learning. It recognizes errors by comparing all incoming text to the many examples of correct text it has seen. If it finds something that is nearly identical, but not completely identical, to a frequent pattern, it marks it as an error. This way, it is able to mark errors where other spelling correctors typically fail, like the difference between to, two and too, or the difference between there, their and they're.

Media attention

You are what you tweet

Thesis prize



Hun as syntactical subject

My work at Davinci