sentiment

33 sentiment analysis a baseline algorithm sentiment

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Unformatted text preview: ­ Third Conference on Email and An+ ­Spam. K. ­M. Schneider. 2004. On word frequency informa+on and nega+ve evidence in Naive Bayes text classifica+on. ICANLP, 474 ­485. JD Rennie, L Shih, J Teevan. 2003. Tackling the poor assump+ons of naive bayes text classifiers. ICML 2003 •  Binary seems to work beXer than full word counts •  This is not the same as Mul+variate Bernoulli Naïve Bayes •  MBNB doesn’t work well for sen+ment or other text tasks •  Other possibility: log(freq(w)) 29 Dan Jurafsky Cross ­Valida%on Iteration •  Break up data into 10 folds •  (Equal posi+ve and nega+ve inside each fold?) 1 Test Training 2 Training Test •  For each fold •  Choose the fold as a temporary test set •  Train on 9 folds, compute performance on the test fold •  Report average performance of the 10 runs 3 4 5 Training Test Training Training Training Test Test Dan Jurafsky Other issues in Classifica%on •  MaxEnt and SVM tend to do beXer than Naïve Bayes 31 Dan Jurafsky Problems: What makes reviews hard to classify? •  Subtlety: •  Perfume review in Perfumes: the Guide: •  “If you are reading this because it is your darling fragrance, please wear it at home exclusively, and tape the windows shut.” •  Dorothy Parker on Katherine Hepburn •  “She runs the gamut of emo+ons from A to B” 32 Dan Jurafsky Thwarted Expecta%ons and Ordering Effects •  “This film should be brilliant. It sounds like a great plot, the actors are first grade, and the suppor+ng cast is good as well, and Stallone is aXemp+ng to deliver a good performance. However, it can’t hold up.” •  Well as usual Keanu Reeves is nothing special, but surprisingly, the very talented La...
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This document was uploaded on 02/14/2014.

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