sentiment

Sentiment

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Unformatted text preview: afsky Bing Liu Opinion Lexicon Minqing Hu and Bing Liu. Mining and Summarizing Customer Reviews. ACM SIGKDD ­2004. •  Bing Liu's Page on Opinion Mining •  hXp://www.cs.uic.edu/~liub/FBS/opinion ­lexicon ­English.rar •  6786 words •  2006 posi+ve •  4783 nega+ve 39 Dan Jurafsky Sen%WordNet Stefano Baccianella, Andrea Esuli, and Fabrizio Sebas+ani. 2010 SENTIWORDNET 3.0: An Enhanced Lexical Resource for Sen+ment Analysis and Opinion Mining. LREC ­2010 •  Home page: hXp://sen+wordnet.is+.cnr.it/ •  All WordNet synsets automa+cally annotated for degrees of posi+vity, nega+vity, and neutrality/objec+veness •  [es+mable(J,3)] “may be computed or es+mated” !Pos 0 Neg 0 Obj 1 ! •  [es+mable(J,1)] “deserving of respect or high regard” !Pos .75 Neg 0 Obj .25 ! Dan Jurafsky Disagreements between polarity lexicons Christopher PoXs, Sen+ment Tutorial, 2011 Opinion Lexicon MPQA Opinion Lexicon General Inquirer Sen%WordNet LIWC 41 33/5402 (0.6%) General Inquirer Sen%WordNet LIWC 49/2867 (2%) 1127/4214 (27%) 12/363 (3%) 32/2411 (1%) 1004/3994 (25%) 9/403 (2%) 520/2306 (23%) 1/204 (0.5%) 174/694 (25%) Dan Jurafsky Analyzing the polarity of each word in IMDB PoXs, Christopher. 2011. On the nega+vity of nega+on. SALT 20, 636 ­659. •  •  •  •  •  How likely is each word to appear in each sen+ment class? Count(“bad”) in 1 ­star, 2 ­star, 3 ­star, etc. But can’t use raw counts: Instead, likelihood: P(w | c) = f (w, c) "w!c f (w, c) Make them comparable between words •  Scaled likelihood: P(w | c) P(w ) Dan Jurafsky Analyzing the polarity of each word in IMDB PoXs, Christopher. 2011. On the nega+vity of nega+on. SALT 20, 636 ­659. POS good (883,417 tokens) amazing (103,509 tokens) great (648,110 tokens) Pr(c|w) Scaled likelihood P(w|c)/P(w) 0.28 awesome (47,142 tokens) l 0.27 l l 0.17 0.17 l l l 0.16 l 0.12 0.1 0.08 l l l l l l l l l 0.11 l l l l l l l l l 1 2 3 4 5 6 7 8 9 10 l l l l 1 0.05 2 3 4 5 0.05 6 l 7 8 9 10 1 l l l l 2 3 l l l 4 5 6 7 8 9 10 l l l 1 0.05 2 3 4 5 6 7 8 9 10 Rating NEG good (20,447 tokens) depress(ed/ing) (18,498 tokens) bad (368,273 tokens) terrible (55,492 tokens) 0.21 Pr(c|w) Scaled likelihood P(w|c)/P(w) 0.28 0.16 l l...
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This document was uploaded on 02/14/2014.

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