Chris Potts: Sentiment analysis in context
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Sentiment analysis in context: Chris Potts, Stanford Computational Linguistics professor
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Chris Potts: Sentiment analysis in context
- 5. Studying Affective Transitions Experience Project mood status corpus
- 7. Emotion Transitions log-scaleCTP(a,b) bewildered artistic curious bouncy chill distressed disappointed excited bewildered crushed chill chipper lazy bitchy bored chipper blissful bouncy energetic flirty numb lonely devastated angry melancholy loved amazing good excited amazed amused anxious blah cheerful depressed happy 0.03 0.04 0.06 0.1
- 9. Worried
- 10. Hopeful
- 11. Blessed
- 12. Social Forces!
- 14. Review Ratings Rating Reviews 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 0.3K 1.3K 2.5K 3.3K 4.8K 7.9K negative (18% of reviews) positive (25% of reviews) neutral (58% of reviews) 2.9 million reviews
- 15. Review Sequence Transitions negative neutral positive positive neutral negative 0.07 0.21 0.51 0.4 0.67 0.44 0.53 0.12 0.04 (a) All product sequences
- 16. negative neutral positive positive neutral negative 0.27 0.27 0.27 0.56 0.56 0.56 0.17 0.17 0.17 negative neutral positive positive neutral negative 0.05 0.11 0.35 0.3 0.6 0.47 0.66 0.28 0.18 Review Sequence Transitions negative neutral positive positive neutral negative 0.07 0.21 0.51 0.4 0.67 0.44 0.53 0.12 0.04 (a) All product sequences (b) High-variance sequences (c) Randomized sequences
- 17. Classification Experiments Conditional Random Field (CRF) vs. MaxEnt
- 19. In an hour from now… I actually kind of liked it. Bla bla … sentiment … bla bla bla … networks … Dude, that was even more boring than his gray shirt, eh?! Yeah right. Great talk… He didn’t even talk about deep learning.
- 20. Modeling person-to-person opinions: The NLP approach Bla bla … sentiment … bla bla bla … networks … Dude, that was even more boring than his gray shirt, eh?! I actually kind of liked it. Yeah right. Great talk… He didn’t even talk about deep learning.
- 21. Modeling person-to-person opinions: The social-network–analysis approach Social balance theory “The enemy of my enemy is my friend” “The enemy of my friend is my enemy” “The friend of my friend is my friend”
- 22. Modeling person-to-person opinions: A unified view – ++ – “The friend of my enemy is my enemy” – Yeah right. Great talk… He didn’t even talk about deep learning. Social balance theory
- 23. Modeling person-to-person opinions: A unified view + “The friend of my friend is my friend” + + The talk was amazing! I couldn’t attend — I was stuck on the Autobahn. Social balance theory
- 24. Model Represent social network as a signed graph: G = (V, E, p, x) } fully observed partially observed x 2 {0, 1}|E| p 2 [0, 1]|E| text-based sentiment predictions edge signs 0 = – 1 = + Task: Infer unobserved portion of x “Boring!” v 2 V e1 2 E x1 = 0 p1 = 0.04 x4 = ? p4 = 0.55 “Okay.”
- 25. We want to infer unobserved portion of such that we 1. agree with text-based sentiment predictions, i.e., 2. get triangles in line with social theories Trade-off: Objective function x x ⇡ p T } } Cost for deviating from text-based sent. prediction Cost of triangle type xt 2 {0, 1}3 EDGE COST TRIANGLE COST x⇤ = arg minx2{0,1}|E| X e2E |xe pe| + X t2T d(xt)
- 26. HL-MRF (Broecheler et al., 2010) • Markov random field (MRF) with continuous variables • Potentials are sums of hinge-loss terms of linear functions of variables. • Relaxed objective function equal to original formulation when is binary, i.e., , • but interpolates over continuous domain . • Objective function convex. • Efficiently solvable. Relaxation as hinge-loss Markov random field (HL-MRF) x 2 {0, 1}|E| [0, 1]|E| x . . x
- 29. Thank you!