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LLM-based sentiment analysis of Hacker News posts between Jan 2020 and June 2023

outerbounds.com

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Re: LLM-based sentiment analysis of Hacker News posts between Jan 2020 and June 2023

#4

Post by vtuulos »

anonu wrote: post #2

Great analysis. How is divisiveness actually calculated?

search "divisive" here: https://github.com/outerbounds/hacker-news-sentiment/blob/ma...

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.

Re: LLM-based sentiment analysis of Hacker News posts between Jan 2020 and June 2023

#7

Post by tantalor »

Is this just using LLM to be cool? How does pure LLM with basic "In the scale between 0-10 ..." prompt stack up against traditional, battle-tested sentiment analysis tools?

Gemini suggests NLTK and spaCy

https://www.nltk.org/

https://spacy.io/

Re: LLM-based sentiment analysis of Hacker News posts between Jan 2020 and June 2023

#9

Post by samstave »

>>Use the tool below to explore various topics and the sentiments they evoke.

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.

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