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Tagger News takes a subset of HN articles, analyzes using ML, and applies tags

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Re: Tagger News takes a subset of HN articles, analyzes using ML, and applies tags

#61
post #59

Earlier quoted context omitted.

You mean Naive Bayes? Because it can't account for interactions between the effects of multiple words.

Just add some "magic", e.g. per response analysis and inter-response rule-based system.

If you keep adding "magic" and doing careful research on what magic works and what doesn't, you end up roughly with the modern field of machine learning.

Random forests are a method that's often effective in taking into account many interactions among high dimensional data.

Re: Tagger News takes a subset of HN articles, analyzes using ML, and applies tags

#62

Earlier quoted context omitted.

Or maybe the algorithm is scary accurate and has correctly deduced that Richard Stallman is, in fact, Satoshi Nakamoto! Somebody contact Newsweek!

As a sincere question, why did people downvote this comment? I thought he was funny and I think lighthearted humor has value to it. It didn't seem snarky to me, did it to someone else? Did it seem off-topic? It's a joke rather than useful information, but I'd argue that it is on-topic per the rules: "Anything that good hackers would find interesting." Am I missing something?

Note: While I'm replying to you, please note that I'm not claiming you've done anything like this or that you are doing these things. Rather, it's just your comment sparked these thoughts. That is all. =)

Since this topic is already fairly meta already, and because of the nature of your question, I'll chime in here as well as to why I would normally down vote your comment in other threads.

"why did people downvote this comment?"

Any discussion of voting (outside of a few exceptions such as this) gets down voted quickly. Not only is it discouraged in the guidelines, it's also generally self-correcting. I've seen far too many comments that ask why they are down voted when they clearly have more votes up than down. In addition, the conversations in reply generally revolved around why people might be voting down, and whether that is wrong.

Basically, it creates a bunch of useless commentary for no good reason.

In addition to this, asking people why they voted down a comment is annoying. The goal of commentary should be to spark either conversation or thought. If it does neither, it's really not worth my trouble to explain why I down vote it. I vote down the comment because it is a bad comment, and not worthy of worthwhile discussion.

I've voted comments up that I disagree with because the discussions they've sparked were interesting and voted down comments I agree with because they don't honestly contribute to the active discussion and exchange of ideas.

Not everyone thinks this way. I'm sure people vote up what they agree with and vote down with what they disagree with without a care to the overall discussion simply to fit an agenda. I admit I've done it in the past (I am not perfect, after all), and I've regretted it. But overall voting corrects itself, and frankly, it does not matter. Karma is representative of your value.

If you are that concerned about the karma of a comment, do not post. If people are voting down your comment and not replying, start by addressing the failings in your comment to spark proper discussion.

Blaming others (tripe like "people would rather vote down than explain where I am wrong") is weak and childish, and will get voted down without hesitation. HN should be better than that weak (non-existent?) rhetoric, and the moment you add that to a comment, you've lost.

Re: Tagger News takes a subset of HN articles, analyzes using ML, and applies tags

#63
post #59

Earlier quoted context omitted.

Just add some "magic", e.g. per response analysis and inter-response rule-based system.

If you keep adding "magic" and doing careful research on what magic works and what doesn't, you end up roughly with the modern field of machine learning. Random forests are a method that's often effective in taking into account many interactions among high dimensional data.

Expert Systems "magic" predates neural networks by decades, being predictable and giving validable results (unlike most ML models).

Re: Tagger News takes a subset of HN articles, analyzes using ML, and applies tags

#64
post #45

Earlier quoted context omitted.

Not too mention a lot of the content has strayed away from technology and has gone towards mental health, personal growth, news, politics, etc.

I don't remember a time when all those topics were not common.

Maybe now that I'm older I notice it more than I did when I first joined.

Re: Tagger News takes a subset of HN articles, analyzes using ML, and applies tags

#65
Not to hijack, but this is similar to a small ML project a friend and I built. It takes news headlines from a bunch of sources and classifies them by common topic. We took a lot longer than a day to build it, though. ;)

It refreshes with new stories every few hours. You can check it out here: http://headlinr.herokuapp.com/

EDIT: click on the bubbles to see individual headlines. Also, here's the GitHub page: https://github.com/dgarrick/headliner

Re: Tagger News takes a subset of HN articles, analyzes using ML, and applies tags

#66
post #58

Earlier quoted context omitted.

http://searchhn.com is a small demo we have been building at Searchera ( https://searchera.io )

IMO I find the domain to user facet lookup to be more useful that the tagging option - I am sure you can just deduce tags from that alone on 90% of the submissions - good demo.

Thank you !

Re: Tagger News takes a subset of HN articles, analyzes using ML, and applies tags

#67

Direct URL to project details: https://devpost.com/software/tagger-news A few comments: 1. To other commenters, as with the HN Vue demo a week ago ( https://news.ycombinator.com/item?id=14284877 ), the project is a technical proof-of-concept; the aesthetics aren't the primary focus. 2. The Algolia API is better for scraping because it allows for bulk requests, unlike the official API (my old 2014 script still works I…

One of the devs here. 1. That's the way we were thinking about it :) 2. Oh, excellent! We hadn't found that or we'd have used it, and we'll start working with it. 3. Tomorrow I'm going to blog about how we approached the machine learning. Short version; we manually came up with regular expressions to classify a training set based on titles . The idea is that when we experimented with manual annotations on titles, the…

I've now blogged in more detail about building Tagger News- check it out here! https://news.ycombinator.com/item?id=14343854

Re: Tagger News takes a subset of HN articles, analyzes using ML, and applies tags

#69

Stop trying to remake Hacker News. It's pretty much perfect the way it is.

it's difficult to find more mobile unfriendly page nowadays than Hacker News

Materialistic on Android makes it decent, but would still prefer something like Boost for Reddit for comments

Re: Tagger News takes a subset of HN articles, analyzes using ML, and applies tags

#70

Direct URL to project details: https://devpost.com/software/tagger-news A few comments: 1. To other commenters, as with the HN Vue demo a week ago ( https://news.ycombinator.com/item?id=14284877 ), the project is a technical proof-of-concept; the aesthetics aren't the primary focus. 2. The Algolia API is better for scraping because it allows for bulk requests, unlike the official API (my old 2014 script still works I…

One of the devs here. 1. That's the way we were thinking about it :) 2. Oh, excellent! We hadn't found that or we'd have used it, and we'll start working with it. 3. Tomorrow I'm going to blog about how we approached the machine learning. Short version; we manually came up with regular expressions to classify a training set based on titles . The idea is that when we experimented with manual annotations on titles, the…

The Awful Reign of the Red Delicious (2014) (theatlantic.com) is tagged 'Microsoft' 'Apple'

Might wanna tweak that...

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