Deeply Moving: Deep Learning for Sentiment Analysis
nlp.stanford.edu
Deeply Moving: Deep Learning for Sentiment Analysis
1–10 of 47 posts
Re: Deeply Moving: Deep Learning for Sentiment Analysis
#2"In constrast, our new deep learning model actually builds up a representation of whole sentences based on the sentence structure. It computes the sentiment based on how words compose the meaning of longer phrases. This way, the model is not as easily fooled as previous models."
Wait, that is all that's necessary to invoke the term "deep learning"?? Wow. Wikipedia seems to agree. I had thought there was something more to this buzzword than "slightly less shallow heuristic guesswork than the AI you already know and love".
Re: Deeply Moving: Deep Learning for Sentiment Analysis
#3"In constrast, our new deep learning model actually builds up a representation of whole sentences based on the sentence structure. It computes the sentiment based on how words compose the meaning of longer phrases. This way, the model is not as easily fooled as previous models." Wait, that is all that's necessary to invoke the term "deep learning"?? Wow. Wikipedia seems to agree. I had thought there was something mor…
To possibly clarify, the way sentences are structured is itself learned, rather than relying on hard-coded rules.
Maybe you still find deep-learning disappointing, but there have been some successes from having the computer learn multiple levels of internal structure/representation from data.
Re: Deeply Moving: Deep Learning for Sentiment Analysis
#4"In constrast, our new deep learning model actually builds up a representation of whole sentences based on the sentence structure. It computes the sentiment based on how words compose the meaning of longer phrases. This way, the model is not as easily fooled as previous models." Wait, that is all that's necessary to invoke the term "deep learning"?? Wow. Wikipedia seems to agree. I had thought there was something mor…
Deep learning is just a technique to do learning in (possibly many) layers.
And I disagree that trying to actually understand sentences is merely "slightly less shallow" than existing sentiment analysis systems (which, for the most part, treat sentences a just a bunch of words, count the positive and negative words, and take the average).
Re: Deeply Moving: Deep Learning for Sentiment Analysis
#5If 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 methods.
Edit: thanks gallamine, I fixed the link
Re: Deeply Moving: Deep Learning for Sentiment Analysis
#6"In constrast, our new deep learning model actually builds up a representation of whole sentences based on the sentence structure. It computes the sentiment based on how words compose the meaning of longer phrases. This way, the model is not as easily fooled as previous models." Wait, that is all that's necessary to invoke the term "deep learning"?? Wow. Wikipedia seems to agree. I had thought there was something mor…
[deleted]
Re: Deeply Moving: Deep Learning for Sentiment Analysis
#7FYI, since it's not exactly obvious: in the live demo (http://nlp.stanford.edu:8080/sentiment/rntnDemo.html), you can double-click on the tree image that it generates to see a zoomed-in version with more information, and correct improperly labeled nodes to help the algorithm learn. Try it with some Rotten Tomatoes review snippets - from the ones I tried, it got almost all of them right.
Re: Deeply Moving: Deep Learning for Sentiment Analysis
#8Fascinating 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.
Re: Deeply Moving: Deep Learning for Sentiment Analysis
#9If 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…
Dave, you're missing the ending "l" on the URL.
Re: Deeply Moving: Deep Learning for Sentiment Analysis
#10"In constrast, our new deep learning model actually builds up a representation of whole sentences based on the sentence structure. It computes the sentiment based on how words compose the meaning of longer phrases. This way, the model is not as easily fooled as previous models." Wait, that is all that's necessary to invoke the term "deep learning"?? Wow. Wikipedia seems to agree. I had thought there was something mor…
To possibly clarify, the way sentences are structured is itself learned, rather than relying on hard-coded rules. Maybe you still find deep-learning disappointing, but there have been some successes from having the computer learn multiple levels of internal structure/representation from data.
The problem is that many sentences (at least in English) exhibit some structural ambiguity (more than one valid syntax tree). Also you have to take into account pragmatics and sociolinguistic factors (like variation), and so on. You can't just feed a formal grammar to a program and expect it to correctly parse English. We're still a LONG way from being able to correctly parse all (or even 99%) of possible English sentences.