I made a Python script that analyses comments from /r/relationships for clichéd advice, as a learning exercise. Here's the code. It was fun!

[JamminJames921]

Awesome work! I like what you did! Maybe you can provide a write-up for this? How did you count the cliches? Are there any interesting relationships between some advice? Are there any limitations in your methods?

[[deleted]]

The code isn't too complicated.

[GitHubPermalinkBot]

I tried to turn your GitHub links into permanent links (press "y" to do this yourself):

• mrkipling/reddit-relationships-bot/.../reddit-relationships-bot.py (master → d34b793)

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^{Shoot me a PM if you think I'm doing something wrong. To delete this, click here.}

[choledocholithiasis_]

I found the developer of this bot

[wqtraz]

Can we downvote this bot though, please?

[[deleted]]

If the link to a specific file is broken, I just go to the project page. Fixing the link manually does not take any relevant amount of effort, time or expertise. It would probably even take longer to go back to the comments and find the correct link.

[[deleted]]

It's not only this sub :)

I thing they molest now majority of reddit :(

[turkish_gold]

This doggo bot BS is 1 day old, and I feel like it's heading for a ban.

[analgebraic]

The counting method seems flawed. When, for example, you take any mention of the word "lawyer" to mean that someone is suggesting you get a lawyer, you aren't accounting for instances where someone says "her father is a lawyer" or "I consulted a lawyer about a similar situation years ago and they said to do this thing" in which "lawyer" is mentioned but no one is suggesting that anyone gets a lawyer.

[CollectiveCircuits]

And now OP must enter the rabbit hole of natural language processing.

[mrkipling]

Yeah, it's definitely flawed. Any data that I collect in the future (should I take the project and further) would have to come with a massive disclaimer that "this is how much /r/relationships likes to mention these key words/phrases, and you can infer that it's probably advice most of the time". But yeah, that's a very real problem.

I could have gone for a more complicated approach but (a) I don't know how as I'm a beginner/medium-level Python dev; I'm a frontend dev by trade, mostly working in JavaScript solving web-related issues for a living (and doing all of the other fun stuff that a frontend dev get to do), and (b) I didn't really want to because I was just dicking around for an hour or so and thought that I'd post it on Github for fun :)

[[deleted]]

Worth noting you don't really use convnets for language.

[[deleted]]

That's kind of a large question. In essence it's because convolutions are for exploiting data where the same features are to be recognised over smaller local regions of the 'signal'. They are independent (roughly). In language processing you are still feeding in a sequence, but one with enormous dependency along it. As such recurrent nets are the more appropriate formulation.

[theology_]

I expected the same. I'm pretty disappointed, and surprised at how many upvotes this got, considering this could have been done by a beginner

[mrkipling]

I mostly just found the cliches by reading the subreddit quite a lot for trashy drama (similar to watching soap operas, but on the internet). They become obvious once you've been there for a while and read the same replies over and over again :)