Hacker News Reader

Comment by humanistbot | Hacker News Reader

There already is a project trying to use NLP to detect the sentiment of scientific citations: https://scite.ai/

This seems to be very hard to pull off, though, given the way people regularly cite work in some scientific domains. For instance, unless specifically comparing to an approach, researchers in ML rarely endorse or disavow a specific approach (just my observation in graph learning).

Yes, using NLP (DL) techniques makes more sense than to require every author to annotate their work.

Running some superficial "sentiment analysis" neural net over scholarly work and basing any sort of analysis on that is a terrible idea for obvious reasons.

It is already a terrible idea for content moderation / sorting reviews / etc, and the stakes are lower.

Don't understand why this keeps being downvoted. NLP is still in its infancy when it comes to detecting nuances of human communication. These are valid objections.

Moreover, scientific citations are amongst the worst candidates for sentiment analysis since authors often express no sentiment about cited papers at all, and often prefer to use dry, neutral tones when describing a paper's shortcomings.

You might need both general intelligence and a deep knowledge of the field to conclude an author noting that X discovered Y on the assumption of Z is not a neutral description but a stark emphasis of their differences, or possibly even straw-manning the original paper.

the goal is ease of use though, not serious analysis

I agree, Natural Language Processing is the right tool and field for this. What is the purpose of language, if you as an author must add an extra layer of metadata to it to convey meaning. Or maybe academic authors should just bute the billet and start using smileys to convey sentiment. These would be easy to work with.

Reply on news.ycombinator.com