Structured sentiment analysis has been around for a while - meaning models that take into account the overall document as more than just a bag of words (reference below to a 2007 Google paper). There are a surprising number of challenges for those new to the problem, e.g. sarcasm or idioms. It's kind of fun to work on.
Deeply Moving: Deep Learning for Sentiment Analysis
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Re: Deeply Moving: Deep Learning for Sentiment Analysis
#12I know there's deep hate for java around here, but here's another relevant link. (You could probably shoehorn in scala or clojure as well) This is a direct tutorial for doing deep learning.
http://nlp.stanford.edu/courses/NAACL2013/
Take a look at a recent homework assignment they had to do with Named Entity Recognition and Deep Learning: http://nlp.stanford.edu/~socherr/pa4_ner.pdf
It's good to see neural networks being leveraged as they are. Conditional Random Fields have been dominant for sequence based classifiers for a while. We've been needing to push the envelope a bit further.
Re: Deeply Moving: Deep Learning for Sentiment Analysis
#13Earlier quoted context omitted.
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 sen…
It does not depend on hard-coded rules. It learns various probabilities for sentence structures. The result is much more flexible than a formal grammar.
Re: Deeply Moving: Deep Learning for Sentiment Analysis
#14Earlier quoted context omitted.
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 sen…
It does not depend on hard-coded rules. It learns various probabilities for sentence structures. The result is much more flexible than a formal grammar.
True, but that still means you require some decent priors about the text that someone reading it might have. People generally have an idea of what the person who wrote it might be like, what sort of things to disregard, and so on. My point was that learning the "structure" of the language is not enough. I don't know exactly how this program works though, so it might be decent at that, I haven't read up on it enough.
Re: Deeply Moving: Deep Learning for Sentiment Analysis
#15Earlier quoted context omitted.
It does not depend on hard-coded rules. It learns various probabilities for sentence structures. The result is much more flexible than a formal grammar.
True, but that still means you require some decent priors about the text that someone reading it might have. People generally have an idea of what the person who wrote it might be like, what sort of things to disregard, and so on. My point was that learning the "structure" of the language is not enough. I don't know exactly how this program works though, so it might be decent at that, I haven't read up on it enough.
The program doesn't need to know priors, if you make sure the priors for the test set are close to the same as the ones for the training set :)
Re: Deeply Moving: Deep Learning for Sentiment Analysis
#16Fascinating 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.
It doesn't look like this one was able to sort those two out either. It gave me strongly neutral signals for both sentences, but that may be because 'shit' isn't currently tagged.
Re: Deeply Moving: Deep Learning for Sentiment Analysis
#17Wasn'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.
Re: Deeply Moving: Deep Learning for Sentiment Analysis
#18Fascinating 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 the shit" is a very specific idiom. Unless the engine has been specifically trained to understand it, it is not surprising that it doesn't know what to do with it.
Re: Deeply Moving: Deep Learning for Sentiment Analysis
#19Wasn'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.
You mean, stocktwits?
Re: Deeply Moving: Deep Learning for Sentiment Analysis
#20Fascinating 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".