Sentiment Analysis Symposium 2015: Syntax
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Transcript of Sentiment Analysis Symposium 2015: Syntax
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SyntaxSentiment Analysis Symposium
Jeff Catlin
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Meaning
Semantics + Syntax + Context = Meaning
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Semantics
• Definition of a word• Many possible definitions• Dependent on syntax and context
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Context
• Who is saying this?• What have they said in the past?• What is the space they’re
talking about
SICK !
SICK !
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Syntax
• What we’re going to be focusing on• The effect of sentence structure on the
meaning of a word or phrase.
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Simple Example
Billy hit the ball over the house.
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Solution
• Humans naturally parse syntax– Billy hit the ball over the house.
• So, learn like a human:– Unsupervised learning across large corpora of text to extract common associations
• Deep learning/Neural Nets• Matrix Factorization
• Bob is going to the store for milk.– You’re not going to see “Milk store closed on Good Friday” in the large corpus – so you
know he’s going to go buy milk.
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Syntax-Heavy Examples
Document Sentiment• I was expecting a great experience, but the waiter was awful.• The staff helped me with everything I needed help with, but didn't make me feel helpless.
Entity Sentiment• I love Coca Cola but hate Pepsi. • Apple was doing bad until Steve Jobs returned.• Because Apple was doing bad, Steve Jobs returned.• Apple was doing bad because Steve Jobs returned.• I wish GM created a new, great car.• GM created a new, great car.
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Apple was doing bad because Steve Jobs returned.
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Because Apple was doing bad, Steve Jobs returned.
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Summary• Semantics + Syntax + Context = Meaning• Many sentences have many valid parses, but that are nonsense for a human• So, use unsupervised learning to understand a valid parse
– John went to the store for milk.
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