negative_nouns = spanish_tools.read_file_to_list(this_path + 'comments/classifier//vocabularies/words/negative_nouns.txt')
positive_others = spanish_tools.read_file_to_list(this_path + 'comments/classifier//vocabularies/words/positive_others.txt')
negative_others = spanish_tools.read_file_to_list(this_path + 'comments/classifier//vocabularies/words/negative_others.txt')
adversative_conj = spanish_tools.read_file_to_list(this_path + 'comments/classifier//vocabularies/words/adversative_conjunctions.txt')
# Comparison is made without accents to increase precision
positive_words = positive_adjectives + positive_adverbs + positive_verbs + positive_nouns + positive_others
for i, s in enumerate(positive_words):
positive_words[i] = spanish_tools.remove_accents(s)
negative_words = negative_adjectives + negative_adverbs + negative_verbs + negative_nouns + negative_others
for i, s in enumerate(negative_words):
negative_words[i] = spanish_tools.remove_accents(s)
# Feature list vector for Morphosyntactic Model and Classifier
feature_list = spanish_tools.read_file_to_list(this_path + 'comments/classifier//vocabularies/features/morphosyntactic_feature_list.txt')
# Feature list vector for Bigram Model and Classifier
bigram_feature_list = spanish_tools.read_file_to_list(this_path + 'comments/classifier//vocabularies/features/bigram_feature_list.txt')
# SVM Classifiers
classifier_svm = Classifier.load(this_path + 'comments/classifier/objects/classifiers/SVM/Morphosyntactic_Classifier_5_Classes_backup')
classifier_svm_3 = Classifier.load(this_path + 'comments/classifier/objects/classifiers/SVM/Classifier_SVM_2_classes')
classifier_svm_1_2 = Classifier.load(this_path + 'comments/classifier/objects/classifiers/SVM/Classifier_SVM_segmented_1_2')
classifier_svm_2_3 = Classifier.load(this_path + 'comments/classifier/objects/classifiers/SVM/Classifier_SVM_segmented_2_3')
classifier_svm_3_4 = Classifier.load(this_path + 'comments/classifier/objects/classifiers/SVM/Classifier_SVM_segmented_3_4')
classifier_svm_4_5 = Classifier.load(this_path + 'comments/classifier/objects/classifiers/SVM/Classifier_SVM_segmented_4_5')
# MNB Classifiers
classifier_mnb = Classifier.load(this_path + 'comments/classifier/objects/classifiers/MNB/Bigram_2500_Classifier_5_Classes')
positive_adverbs = spanish_tools.read_file_to_list(this_path + '/vocabularies/words/positive_adverbs.txt')
negative_adverbs = spanish_tools.read_file_to_list(this_path + '/vocabularies/words/negative_adverbs.txt')
positive_verbs = spanish_tools.read_file_to_list(this_path + '/vocabularies/words/positive_verbs.txt')
negative_verbs = spanish_tools.read_file_to_list(this_path + '/vocabularies/words/negative_verbs.txt')
positive_nouns = spanish_tools.read_file_to_list(this_path + '/vocabularies/words/positive_nouns.txt')
negative_nouns = spanish_tools.read_file_to_list(this_path + '/vocabularies/words/negative_nouns.txt')
positive_others = spanish_tools.read_file_to_list(this_path + '/vocabularies/words/positive_others.txt')
negative_others = spanish_tools.read_file_to_list(this_path + '/vocabularies/words/negative_others.txt')
adversative_conj = spanish_tools.read_file_to_list(this_path + '/vocabularies/words/adversative_conjunctions.txt')
# Comparison is made without accents to increase precision
positive_words = positive_adjectives + positive_adverbs + positive_verbs + positive_nouns + positive_others
for i, s in enumerate(positive_words):
positive_words[i] = spanish_tools.remove_accents(s)
negative_words = negative_adjectives + negative_adverbs + negative_verbs + negative_nouns + negative_others
for i, s in enumerate(negative_words):
negative_words[i] = spanish_tools.remove_accents(s)
# Feature list vector for Morphosyntactic Model and Classifier
feature_list = spanish_tools.read_file_to_list(this_path + '/vocabularies/features/morphosyntactic_feature_list.txt')
# Feature list vector for Bigram Model and Classifier
bigram_feature_list = spanish_tools.read_file_to_list(this_path + '/vocabularies/features/bigram_feature_list.txt')
# SVM Classifiers
classifier_svm = Classifier.load(this_path + '/objects/classifiers/SVM/Morphosyntactic_Classifier_5_Classes_backup')
# MNB Classifiers
classifier_mnb = Classifier.load(this_path + '/objects/classifiers/MNB/Bigram_2500_Classifier_5_Classes')
print("Classes: " + repr(classifier.classes))
#performance = kfoldcv(NB, vectors, folds=n_fold)
performance = kfoldcv(type(classifier), vectors, folds=n_fold)
print("Accuracy: %.3f\n" \
"Precision: %.3f\n" \
"Recall: %.3f\n" \
"F1: %.3f\n" \
"Stddev:%.3f" % performance)
print()
print("Confusion matrx:")
print(classifier.confusion_matrix(vectors).table)
classifier.save(trained_filename)
elif options["predict"]:
classifier = Classifier.load(trained_filename)
print("#Author\tURL\tPrediction\tActual")
for v in vectors:
print("%s\t%s\t%s" % (v.name.encode('utf-8'),
repr(classifier.classify(v)),
repr(v.type)))
# Remove any individual documents classified as 'None' prior to
# calculating performance unless the entire set has no classifications,
# in which case we assume we are doing a blind prediction but won't
# calculate performance metrics (eg no rating shown on website,
# so no classification to predict)
pre_filter_n = len(vectors)
fvectors = list(filter(lambda x: x.type is not None, vectors))
post_filter_n = len(fvectors)