sa_out2.log

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#   Showing for <models.svm.SVM object at 0x1052c9f90>
Best params: {'steps': [('vect', TfidfVectorizer(analyzer='word', binary=False, charset='utf-8',
        charset_error='ignore', dtype=<type 'long'>, input='content',
        lowercase=True, max_df=1.0, max_features=None, max_n=None,
        min_df=2, min_n=None, ngram_range=(1, 1), norm='l2',
        preprocessor=<function remove_noise at 0x104787410>,
        smooth_idf=True, stop_words=None, strip_accents=None,
        sublinear_tf=True, token_pattern=u'(?u)\\b\\w\\w+\\b',
        tokenizer=<function tokenize at 0x104787848>, use_idf=False,
        vocabulary=None)), ('clf', LinearSVC(C=1.0, class_weight=None, dual=True, fit_intercept=True,
     intercept_scaling=1, loss='l2', multi_class='ovr', penalty='l2',
     random_state=None, tol=0.0001, verbose=0))]}
Top Features for Model: <models.svm.SVM object at 0x1052c9f90>
dimensionality: 7540
density: 0.987709991158
top 15 keywords per class:
negative: injury, lost, not, :-(, shit, bad, stupid, cancelled, worse, worst,
  sad, hate, sorry, fuck, :(
neutral: trayvon, tallahassee, rush, load, clemson, calling, set, nba, theres,
	yea, jump, vote, 8th, question, paterno
positive: thanks, glad, :), nice, awesome, cool, interesting, amazing, best,
	love, good, fun, happy, excited, great

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# Total Score: 0.655985489722
==============================
             precision    recall  f1-score   support

   negative       0.64      0.34      0.45       340
    neutral       0.65      0.79      0.72       739
   positive       0.66      0.67      0.67       575

avg / total       0.65      0.66      0.64      1654

[[116 148  76]
 [ 36 585 118]
 [ 29 162 384]]
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