Lexalytics CEO Jeff Caitlin's presentation at the 2015 Sentiment Analysis Symposium. The ability to parse syntax correctly is vital for accurate sentiment analysis. It’s not enough for a program to simply weigh individual words for their sentiment. Sentences can contain several different entities towards which different sentiments are being expressed, depending on how the sentence is ordered.

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Syntax
Sentiment Analysis Symposium
Jeff Catlin

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Meaning
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Semantics + Syntax + Context = Meaning

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Semantics
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• Definition of a word
• Many possible definitions
• Dependent on syntax and context

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Context
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• 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
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• 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
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Billy hit the ball over the house.

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Solution
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• 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
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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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