Gemini suggests NLTK and spaCy
Also, many exisiting sentiment analysis tools have a lot of research behind them that can be referenced when interpreting the results (known confounds etc). I don't think there is yet an equivalent for the LLM approach
Its like the linux of operating systems. Sure you can handwrite up some custom OS more specialized for a purpose. But its much easier to just use linux, which everyone understands on a basic level and is extremely robust, and modifying it slightly for the end goal.
And saying "Traditional sentiment analysis" tools are "Battle tested" is laughable. LLMs in the past year alone, probably has 1000x the cumulative usage of all sentiment analysis tools in history.
LLMs get 100 billion + each year in research, improvements, engineering, optimisations.
LLMs keep rapidly improving year to year in capabilities. Sonnet 3.5 already obliterates the original GPT-4 in every aspect.
LLMs keep getting cheaper year to year. Gemini flash is like 100x cheaper than the original GPT3.5.
You can onboard any person who can write python, to start using LLMs to perform language analysis in a day. Versus weeks to use these traditional tools.
Nearly all NLP tasks will be standardised to use LLMs as the baseline default tool. Sure there'll be some short term degradations in some specific aspect, but there's no stopping the tide.
By the way, traditional ML-based translation is also pretty much dead and replaced by LLMs. I've been seeing an explosion in fan-translations done by say Sonnet 3.5, the improvement in fluency and accuracy is just radical and extreme, I often don't even notice the AI-translation anymore.
Its not comparing C against Javascript, its comparing Ada against Javascript. Ada is not going to be any faster than javascript because its too niche and therefore underoptimised.
The theoretical minimum computation required by LLMs is far higher than traditional simple NLP algorithms. But the practical computation cost of LLMs will soon be cheaper, because LLMs get so much investment and use, there's massive full-stack optimizations all the way from the GPU to the end libraries.
I generated title, summary, keywords and hierarchical topics up to 3 levels up from the original text. My plan for now is to put them in a vector search engine, which, incidentally, was made with Sonnet 3.5 with very little iteration. I want to play around to see how I can organize my ideas with LLMs, make something useful from all that text.
I really don't know what I will discover. One small insight I already found is that summarization works really well, you can use summaries instead of full texts to prime Claude and it works better than expected. Unlimited context? Maybe.
Another direction of research is to create a nice taxonomy, there are thousands of topics, pretty difficult task, but there must be a way using clustering and LLMs. That is why I generated topic, parent-topic, gp-topic, and ggp-topic from all snippets. I would probably manually edit the top 2 levels of the taxonomy to give it the right focus.
I'm also integrating with my HN and reddit feeds. X is too stingy with the API. Maybe Pocket and local downloads folder too, I save/bookmark stuff I like. I could also include all the papers I am reading into the corpus. It could synthesize a ranked feed aligned to my own interests.
I’d like to use your project
> Football (206 posts)
Either hacker news really likes the national forensic league, or these LLM-categories are a bit dubious.
Also hmmm:
> American football (7 posts)
> American_football (6 posts)
> But how do people feel about these topics
I find it notable that tokens don't necessarily express people's feelings. Put another way, tokens aren't how people feel, they're how they write.
Samstave mentioned in this thread that Twitter is a 'global sentiment engine'. I'm sure that's literally true. Sentiment measurement is only accurate to the degree that people are expressing their real feelings via tokens. I can imagine various psychological and political reasons for a discrepancy.
If you did sentiment analysis of publicly known writings of North Korean administrators, would that represent their feelings?
I think the interplay with free speech is interesting here: In a setting where people feel socially and legally safe to express their true opinion, sentiment analysis will be more accurate.
We don't try to retrieve articles/topics from the model, which would be affected by the cutoff, just asking it to analyze the sentiment or summarize the content provided in a prompt
edit I realized too late I had the years off. It is pure coincidence of month, not a real data bias. Sorry! I still think it would be interesting to see a 7B comparison but that is just to see how well a small model could spot big trends compared to a bigger one.
This is a cool phrase.
It is personally important as when I was asked in a panel interview @ -- They asked "what do you think Twitter is?
My response was "You're a global sentiment engine""
(There are a lot of conversations I'd love to have with the HN community with respect to our shared experiences, and weird history flipped-bits that exists in the minds of those who experienced that...
like threads of how linux came, or how xml was born through things I touched in a forrest gump way - and how there are so many stories from so many.
You could watch Twitter go from being a niche little new thing to popular to "twitter is trash" too popular to increasingly divisive to the purchase and rename to X to today.
More like a sentiment engine for bot operators.
I started to look into it, but in the little time I had to devote to the idea, I read that the Agolia API lets you look over a longer period, but that it is relatively costly.
I just want to look for all story titles from the beginning of time which match one of several simple search terms, and return submission date and title for an analysis I'd conduct in R.
Am I overthinking it and a simple Python script without an API code can do it?
You can find all titles and dates since the beginning of HN in this public BigQuery dataset: https://console.cloud.google.com/marketplace/product/y-combi...
I also think it *could* be less of a problem than you might think. If we treat the scale as arbitrary (which I think is a safe thing to do), then movement along the scale could be sufficient to ascertain *something*
Great work folks, glad we can all agree on that one.
Interesting that they used an LLM for this. I mean it makes sense and the data seems to pass the pub test but I, in my ignorance, would not have assumed that a language model would be well suited for number crunching.
And no 5s? What is even going on in that LLM?
It's nice to see this scale used outside of The Good Place.
Sentiment of forum posts is not an absolute value, you can't compare it against, for example, conversations in a pub, or talks between friends, etc.
I think they should have normalized the numbers around the average, so to have a relative measurement of the various topics.
LLM's are really sensitive to bad or even slightly ambiguous grammar. I wonder if the numbers would differ significantly with "Reply only with the tags, in the following format".
The semantics of the topics/tags could be improved for sure with a more detailed prompt
> 350M Tokens Don't Lie: Love And Hate In Hacker News, to
> LLM-based sentiment analysis of Hacker News posts, to
> LLM-based sentiment analysis of Hacker News posts between Jan 2020 and June 2023
I was horrified when I read international students as one of top on the hate list. Although I saw a couple of comments attributed their cities housing crises on international students and thought that this sentiment is wide supported.
SENTIMENT 6
:D
that's really "hacker", a worthy first place
I actually spent 10 minutes trying to see if there are obvious tests for U-shaped distributions. I'd love to hear if anyone has ideas here.
For context, I'm someone who uses HN to search for topics I'm interested in, rather than something like Google or Reddit.
- For anything SF community-related, most hits are from 10+ years ago. Lots of "hey we have a space in soma, any local startups want to hang and drink beers?" or "we have an empty desk in a space in the mission, any hackers want to grab it for free?" - all from around 2012 or prior. Nothing like that seems to happen anymore.
- Starting from around 2016, a heavy anti-technology sentiment appears. Cloud, crypto, AI - all are nonsense propagated by VC types and overzealous engineers.
- Similarly, any thread involving money/labor invariably has an anti-capitalist and/or "unions would solve everything" tangent.
Would be interested to hear if others have observed similar.
TFA’s sentiment decrease tracks very closely with the huge uptick in user creation that started in 2022. HN isn’t really a tech site anymore, it’s about vibes. That makes sense given that in 2024 there’s a million places online talking about tech so HN only has its culture to distinguish itself. This wasn’t the case in 2008. The vibes here, along with the older demographics of the site, are increasingly nostalgic and cynical.
It'll all probably go the same way as Slashdot did which went through the same cycle (replace "VC huckster" with "Microsoft" and "surveillance capitalism" with "three letter agencies") until it too gets replaced by a site/community with energetic younger users creating new things.
