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Sentiment Analysis Symposium 2015: Syntax

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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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Sentiment Analysis Symposium 2015: Syntax

  1. 1. © 2015 Lexalytics Inc. All rights reserved Syntax Sentiment Analysis Symposium Jeff Catlin
  2. 2. © 2015 Lexalytics Inc. All rights reserved Meaning 2 Semantics + Syntax + Context = Meaning
  3. 3. © 2015 Lexalytics Inc. All rights reserved Semantics 3 • Definition of a word • Many possible definitions • Dependent on syntax and context
  4. 4. © 2015 Lexalytics Inc. All rights reserved Context 4 • Who is saying this? • What have they said in the past? • What is the space they’re talking about SICK ! SICK !
  5. 5. © 2015 Lexalytics Inc. All rights reserved Syntax 5 • What we’re going to be focusing on • The effect of sentence structure on the meaning of a word or phrase.
  6. 6. © 2015 Lexalytics Inc. All rights reserved Simple Example 6 Billy hit the ball over the house.
  7. 7. © 2015 Lexalytics Inc. All rights reserved Solution 7 • 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.
  8. 8. © 2015 Lexalytics Inc. All rights reserved Syntax-Heavy Examples 8 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.
  9. 9. © 2015 Lexalytics Inc. All rights reserved Apple was doing bad because Steve Jobs returned. 9
  10. 10. © 2015 Lexalytics Inc. All rights reserved Because Apple was doing bad, Steve Jobs returned. 10
  11. 11. © 2015 Lexalytics Inc. All rights reserved 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.
  12. 12. © 2015 Lexalytics Inc. All rights reserved

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