sentiment

Pword1word2 by hitsword1 near word2n2 hitsword1

Info iconThis preview shows page 1. Sign up to view the full content.

View Full Document Right Arrow Icon
This is the end of the preview. Sign up to access the rest of the document.

Unformatted text preview: y Hatzivassiloglou & McKeown 1997 Step 3 •  Supervised classifier assigns “polarity similarity” to each word pair, resul+ng in graph: brutal helpful corrupt nice fair 52 classy irrational Dan Jurafsky Hatzivassiloglou & McKeown 1997 Step 4 •  Clustering for par++oning the graph into two + brutal helpful corrupt nice fair 53 classy  ­ irrational Dan Jurafsky Output polarity lexicon •  Posi+ve •  bold decisive disturbing generous good honest important large mature pa+ent peaceful posi+ve proud sound s+mula+ng straigh•orward strange talented vigorous wiXy… •  Nega+ve •  ambiguous cau+ous cynical evasive harmful hypocri+cal inefficient insecure irra+onal irresponsible minor outspoken pleasant reckless risky selfish tedious unsupported vulnerable wasteful… 54 Dan Jurafsky Output polarity lexicon •  Posi+ve •  bold decisive disturbing generous good honest important large mature pa+ent peaceful posi+ve proud sound s+mula+ng straigh•orward strange talented vigorous wiXy… •  Nega+ve •  ambiguous cau%ous cynical evasive harmful hypocri+cal inefficient insecure irra+onal irresponsible minor outspoken pleasant reckless risky selfish tedious unsupported vulnerable wasteful… 55 Dan Jurafsky Turney Algorithm Turney (2002): Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews 1.  Extract a phrasal lexicon from reviews 2.  Learn polarity of each phrase 3.  Rate a review by the average polarity of its phrases 56 Dan Jurafsky Extract two ­word phrases with adjec%ves First Word Second Word Third Word (not extracted) JJ RB, RBR, RBS JJ NN or NNS RB, RBR, or RBS NN or NNS JJ JJ JJ VB, VBD, VBN, VBG anything Not NN nor NNS Not NN or NNS Nor NN nor NNS anything 57 Dan Jurafsky How to measure polarity of a phrase? •  Posi+ve phrases co ­occur more with “excellent” •  Nega+ve phrases co ­occur more with “poor” •  But how to measure co ­occurrence? 58 Dan Jurafsky Pointwise Mutual Informa%on •  Mutual informa%on between 2 random variables X and Y P( x, y) I( X, Y ) = !! P( x, y) log 2 P ( x )P ( y ) xy •  Pointwise mutual informa%on: •  How much more do ev...
View Full Document

This document was uploaded on 02/14/2014.

Ask a homework question - tutors are online