Sentiment Analysis Symposium 2015: Syntax

© 2015 Lexalytics Inc. All rights reserved

SyntaxSentiment Analysis Symposium

Jeff Catlin

© 2015 Lexalytics Inc. All rights reserved 2

Meaning

Semantics + Syntax + Context = Meaning


Semantics

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


Context

• Who is saying this?• What have they said in the past?• What is the space they’re

talking about

SICK !

SICK !


Syntax

• What we’re going to be focusing on• The effect of sentence structure on the

meaning of a word or phrase.


Simple Example

Billy hit the ball over the house.


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.


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.


Apple was doing bad because Steve Jobs returned.

9


Because Apple was doing bad, Steve Jobs returned.

10


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.


Sentiment Analysis Symposium 2015: Syntax

Data & Analytics

rights reserved apple

rights reserved context

rights reserved semantics

rights reserved solution

steve jobs

syntax billy

milk store

great car