Little Awful Jam 2018: Conclusions and Post-Mortem

It’s done enough!

tl;dr: Download a Runnable Jar Here

Standalone PC/OSX builds are pending.

Kudos to Peter Queckenstedt (@scutanddestroy) for doing an amazing job on the Proctor, Hillary, and Trump.

Post-Mortem:

​This has been a positive experience. I love games that actually have nontrivial interactions in them and completely open-ended text inputs. I’m a fan of interactive fiction, but hate that feeling when you’re digging around and grasping for action words like some sort of textual pixel-hunt.

The language processing systems in DS2016 aren’t particularly complicated, but they’re more simple than I’d like. In the first week of the jam I started writing a recurrent neural network to parse and analyze the sentiment of the player’s comments. I realized, perhaps too late, that there wasn’t enough clean data for me to use to accurately gauge the sentiment and map it to social groups. Instead, I wrote a basic multinomial naive bayes classifier that takes a sentence, tokenizes it, and maps it to ‘like’ or ‘dislike’. Each group has its own classifier and tokenizer, so I could program demographics with a base voting likelihood and give each of them a few sentences on the “agrees with” and “disagrees with” sides, then have them automatically parse and change their feelings towards the player.

A usability change that came in later than one would guess was as follows: I had originally grabbed the demographic with the largest emotional response to a comment and displayed them with the sentiment change. Unfortunately, this turned out to over-exaggerate one particularly noisy group. Another change, shortly thereafter, was masking the exact amount of the change. Instead of saying +1.05% opinion, it simply became “+Conservatives” or “-Hipsters”. This was visually far easier to parse and I think helped the overall readability of the game.

There is still a call to add some more direct public opinion tracking in the game, letting players know in closer to real time how they’re doing among the demographics. I may find it in myself to introduce that.

The last interesting aspect that I noticed during playtesting: I had slightly over-tuned the language models to my style of writing. Instead of opining on matters at any length, people were making enormous run-on sentences which appealed to every demographic at the same time. These statements, often self-contradictory, were not something I expected or handled well. I found the game to be rather difficult, but it looks like playtesters had a dandy time making the states go all blue.