21–30 of 47 posts
Wasn't there a company that did a similar sentiment analysis on Twitter and wanted to trade the stock exchanges on that information? I wonder what happened to that.
Fascinating project, I worked for a data analytics company and we always had a basic test that we'd try to throw at any Sentiment analysis engine. // Negative > This is shit. // Positive > This is the shit. Most engines can't sort that out. I'm definitely going to take a more in-depth look at this.
"This is shit" isn't inherently negative. Consider the question "what is the brown substance in the toilet?" and you can answer a neutral "this is shit".
If you're interested in using deep learning for nlp, I highly suggest putting a pot of coffee on and watching this a couple times: http://www.socher.org/index.php/DeepLearningTutorial/DeepLea... The really neat part is moving away from bag of words representations. Bag of words was always a bit of a hack. But seriously, watch those videos, it will give you a good idea on how it all works, the op is using similar meth…
Are the risks larger because we are successful?
"larger because we are" is marked as positive, "successful" is marked as very positive, the rest are marked as neutral. But the entire sentence is marked as negative (which to me it certainly reads negative).
> I have seen many good movies, this one is not one of them.
> All movies, except this one, are good.
> I thought this movie was going to be bad, but I was wrong.
which rate positive, positive and negative.
I'm full of hope that the modern neural net and linear algebra type methods will eventually crack this though!
It's interesting how the decades of research in computational linguistics still come down to rating a single sentence + or -. And though they are getting better, the systems are still pretty open to 'obvious' attacks: > I have seen many good movies, this one is not one of them. > All movies, except this one, are good. > I thought this movie was going to be bad, but I was wrong. which rate positive, positive and negat…
Does anyone familiar with NLP know how the sentence trees are parsed? Can something like python's nltk do that? I always assumed that was very difficult to do well.