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Deeply Moving: Deep Learning for Sentiment Analysis

nlp.stanford.edu

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Re: Deeply Moving: Deep Learning for Sentiment Analysis

#41

Post by dagw »

byoung2 wrote: post #20
joeblau wrote: post #8

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".

I imagine that someone using the word 'shit' to describe the brown stuff in the toilet is probably negatively inclined towards it. Someone discussing it in a more neutral context (like a doctor or scientist) would probably be more likely to say 'feces'

Re: Deeply Moving: Deep Learning for Sentiment Analysis

#42

Post by dkersten »

hnriot wrote: post #40
dkersten wrote: post #24

This is pretty cool. I love how in some sentences, the actual words or phrases are all marked positive, yet it is able to accurately mark the entire sentence gets correctly marked as negative. For example, from the paypal/mailpile article from the front page: 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 n…

How is being successful negative? If anything it's neutral, being successful is clearly good, having exposure to larger risk is negative, a net neutral.

Being successful is positive, but in the context of the article (paypal not releasing their money) the statement is negative.

Re: Deeply Moving: Deep Learning for Sentiment Analysis

#43

Post by brianshaler »

agibsonccc wrote: post #33
spenuke wrote: post #32

Earlier quoted context omitted.

Yeah, so much fun.

Clever. hehe That aside, has anyone saw any recent works on detecting sarcasm? There's not much advancement in this that I've seen.

This deep learning method is an example from going from bag-of-words to document level analysis. To detect sarcasm, you'd have to make another leap from document analysis to personality modeling. Humans can detect sarcasm because they^H^H^H^Hwe can build a mental model of what is expected to be said—by a particular author or about a specific topic. If a statement is sufficiently contradictory to our mental model and we are sufficiently confident of the predicted sentiment, we know it's sarcasm.

Without knowing who said something or in what context it was said, even humans would fail to accurately detect sarcasm. Here are some hasty examples that could vary, depending on the author or situation:

"Michael Bay really outdid himself on that one." "That Kanye West song is so well-written." "It'd fun to build that PHP."

Re: Deeply Moving: Deep Learning for Sentiment Analysis

#44

Post by agibsonccc »

brianshaler wrote: post #43
agibsonccc wrote: post #33

Earlier quoted context omitted.

Clever. hehe That aside, has anyone saw any recent works on detecting sarcasm? There's not much advancement in this that I've seen.

This deep learning method is an example from going from bag-of-words to document level analysis. To detect sarcasm, you'd have to make another leap from document analysis to personality modeling. Humans can detect sarcasm because they^H^H^H^Hwe can build a mental model of what is expected to be said—by a particular author or about a specific topic. If a statement is sufficiently contradictory to our mental model and…

Very good point. I love the way you framed that. I took some very cursory attempts at doing it at the sentence level based on ngram representations, obviously this was limited.

You also bring up a very good point about humans being able to barely detect it. It's hard to infer from text without tone a good portion of the time.

It'd be neat to see what we could do with tone as an input feature (obviously we couldn't get that most of the time) but maybe if enough tech becomes available that voice inputs are common, sarcasm can be a relevant enough problem to solve.

In general, I know it's not really worth it to try to solve (that I could think of immediately anyways) aside from "just because".

That being said: ambiguity is ambiguous. What can you do aside from approximate as best you can?

Re: Deeply Moving: Deep Learning for Sentiment Analysis

#45

Post by rgonzalez »

I would like to get an accuraye overall result of the sentence. For example this "I found the result of this study amusing since it was far from accurate." was classified as nothing less than neutral but I was trying to see if it would pick up the sarcasm. Is sentiment analysis not yet there?

Re: Deeply Moving: Deep Learning for Sentiment Analysis

#46

Post by bhelx »

ninjin wrote: post #39
bhelx wrote: post #29

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.

Parsing is one of the most competitive areas of research in NLP and yes, you are correct, doing it well is very difficult. State-of-the-art performance on newswire data is around 95% if you score local structure. miket has already replied with a well-established lexical parser. The Stanford parser is one of the most robust out there, you could also go for the BLLIP (or Charniak or Charniak and Johnson) re-ranking par…

thank you ninjin
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