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Comment by Leftium | Hacker News Reader

I have been considering adding labels/filters based on sentiment analysis to my HN reader. I wonder if your classifier-based method would be more effective.

I added filters that blur posts from new HN accounts based on age/karma thresholds (as suggested by another HN user). However, instead collapsing, I just blurred them. This allows quickly peeking via hover. My reader also highlights posts with higher points/comments, and those orange highlights survive the blurring.

This is what blurring looks like: https://hn.leftium.com/newest?min_karma=500&min_age_years=5

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That’s very nice. Feel free to integrate if you want :) Just request https://classify.stylometry.net/violations/49458161 or whatever item ID (as long as it was posted later than a few weeks ago) and it will return the scores for that post/comment and all its children.

wow thanks! I integrated like this: https://hn.leftium.com/i/49449507?violation_threshold=0.5

The url param temporarily sets the threshold, you can permanently set it via the settings.

My initial assessment is 0.5 setting is too aggressive; the posts that I generally don't care to read seem to violate multiple rules at once.

It's a shame we can't see comment scores so orange highlights could shine through the blurring on "good" comments.

Wow that was quick :) Meh, I feel like comment points have become somewhat untethered to comment quality (at least assuming threads are roughly sorted by comment score). It’s a good heuristic usually but it’s wrong often enough for it to be difficult to rely on.

I want to improve the classifier scores too. There’s some parameters in the loss function I could play around with more when training which basically optimize for more false negatives or false positives. Problems with no ground truth are very frustrating lol (though I suppose dang/tomhow’s manually written warnings could be treated as labels instead of using labels from GPT, but I suspect there’s not enough of them).

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