My saltiest comment was reportedly "If taxing price gougers seems stupid you're going to hate the pitchfork-toting mobs." I'm not sure how to work some kind of contextual analysis into it but I'm pretty sure it's something on your to-do list. Good on you for the creative idea and implementation and putting it out here for us to sprinkle salt on.
Re:The Saltiest Users on HN
by Kassius509 on Friday April 19, 2019 @12:52PM
(#19701015)
TYVM.
This kind of feedback gets me pumped about continuing to work on it.
For the curious you can see the dictionary here: https://github.com/sloria/TextBlob/blob/eb08c120d364e9086467...
The package used is a pretty popular one called TextBlob. It is nifty for working with unlabeled data like we have with the HackerNews dataset.
We really focused our definition of saltiness around being a combination of (subjective + negative) comments.
We reduced the impact of (objective + negative) as we feel that criticism, while at times painful, if presented objectively isn't necessarily salty.
We built this model fast (1 week) and have since iterated this week into developing a Fine Tuned BERT model that we are training over a much broader set of toxicity, demographic, and polarity features. The training set is much larger and higher quality so we are expecting a large jump in precision upon deployment.
I hope the app gave you some good chuckles as you went around though. It's hard to explain how excited I felt when I saw pg_is_a_butt at the top of my pandas data frame the first time I processed the data.
It's doing a little bit right. :)