Donald Trump reelected President
Bad: 0.9879 - Good: 0.0129
Show HN: Testing HN titles against a neural network
91–100 of 225 posts
Re: Show HN: Testing HN titles against a neural network
#92This comment is going to collect the most votes yet is predicted to be Bad: 0.9320 - Good: 0.0800
Does this not add up to 1.0 by the way.
I didn’t read the code in the post and don’t have any deep familiarity with machine learning, but I have implemented a naive bayesian classifier to do something similar for tweets. The scores you get from that method don’t add up to 1 either.
Re: Show HN: Testing HN titles against a neural network
#93Tried: Can a neural network predict if your HN post title will get up votes? - Bad: 0.9917 - Good: 0.0076 I think it sums up all.
Show HN: Can a neural network predict if your HN post title will get up votes? Bad: 0.0598 - Good: 0.9307 It's all about that Show HN.
Well
"Show HN:" Bad: 0.0002 - Good: 0.9998
Re: Show HN: Testing HN titles against a neural network
#94AI will take over the world by 2025 - Bad: 0.0000 - Good: 1.0000
That NN predicted.
Re: Show HN: Testing HN titles against a neural network
#95It's basically a buzzword detector. "this is just a tool for detecting buzzwords" => Bad: 0.9991 - Good: 0.0011 "this is merely a device for detecting artificially sophisticated words" => Bad: 0.0019 - Good: 0.9980
That is how it works here, you collect upvotes if you use fancy words. That's why everybody uses the word "orthogonal" here all the time. Have you ever seen that word anywhere else?
Re: Show HN: Testing HN titles against a neural network
#96Re: Show HN: Testing HN titles against a neural network
#97"creating linux network socketss" -> Good 0.99 "creating linux network sockets" -> Good: 0.01
Interestingly, a couple months back I saw a reddit thread where someone collected data that showed that posts with small spelling errors will gain (in some cases significantly) more upvotes - for whatever reason.
Re: Show HN: Testing HN titles against a neural network
#98Fun.
BTW, I tried a bunch of single word titles (example: red, green, blue, title), and I always seem to get the same result: Bad: 0.4528 - Good: 0.5472
So, apparently, if you want to maximize your "score" with the lowest mental effort, just spam thousands of single word title posts, and then, it's a coin flip for each one :)
Re: Show HN: Testing HN titles against a neural network
#99It's basically a buzzword detector. "this is just a tool for detecting buzzwords" => Bad: 0.9991 - Good: 0.0011 "this is merely a device for detecting artificially sophisticated words" => Bad: 0.0019 - Good: 0.9980
That is how it works here, you collect upvotes if you use fancy words. That's why everybody uses the word "orthogonal" here all the time. Have you ever seen that word anywhere else?
isn't that orthogonal to the discussion?
Re: Show HN: Testing HN titles against a neural network
#100I kinda doubt this title would get up votes:
> spam can't ml neural network ai btc crypto
> Bad: 0.0013 - Good: 0.9986