from __future__ import print_function
from __future__ import print_function
import sys
from polarity_classifier import PolarityClassifier
from KafNafParserPy import KafNafParser
if __name__ == '__main__':
files = []
fd = open('nl.list.test')
for line in fd:
files.append(line.strip())
fd.close()
my_polarity_classifier = PolarityClassifier('nl')
my_polarity_classifier.load_models(sys.argv[1])
OK = WR = 1
for example_file in files:
this_obj = KafNafParser(example_file)
my_polarity_classifier.classify_kaf_naf_object(this_obj)
this_obj.dump()
break
GOLD = {}
list_ids_term_ids = []
for opinion in this_obj.get_opinions():
op_exp = opinion.get_expression()
if t is None:
print(' Target: NONE', file=sys.stderr)
else:
print(' Target:', t.to_line(), file=sys.stderr)
if h is None:
print(' Holder: NONE', file=sys.stderr)
else:
print(' Holder:', h.to_line(), file=sys.stderr)
#Remove feature_file feature_file
#Remove also the target file target_features_file
os.remove(feature_file)
os.remove(target_features_file)
os.remove(holder_features_file)
## CREATE THE KAF/NAF OPINIONS
add_opinions(final_triples,kaf_naf_obj)
if args.polarity:
my_polarity_classifier = PolarityClassifier(language)
my_polarity_classifier.load_models(os.path.join(__here__,'polarity_models',language))
my_polarity_classifier.classify_kaf_naf_object(kaf_naf_obj)
kaf_naf_obj.dump()
from polarity_classifier import PolarityClassifier
if __name__ == '__main__':
argument_parser = argparse.ArgumentParser(
description=
'Train a polarity (positive/negative) classifier for opinions',
version='1.0')
argument_parser.add_argument(
'-i',
dest='inputfile',
required=True,
help='Input file with a list of paths to KAF/NAF files (one per line)')
argument_parser.add_argument('-o',
dest='output_folder',
required=True,
help='Folder to store the models')
args = argument_parser.parse_args()
#Load list of files
training_files = []
fd = open(args.inputfile, 'r')
for line in fd:
if line[0] != '#':
training_files.append(line.strip())
fd.close()
print 'Total training files: %d' % len(training_files)
my_polarity_classifier = PolarityClassifier('nl')
my_polarity_classifier.train(training_files, args.output_folder)
#!/usr/bin/env python
import argparse
from polarity_classifier import PolarityClassifier
if __name__ == '__main__':
argument_parser = argparse.ArgumentParser(description='Train a polarity (positive/negative) classifier for opinions',version='1.0')
argument_parser.add_argument('-i', dest='inputfile', required=True, help='Input file with a list of paths to KAF/NAF files (one per line)')
argument_parser.add_argument('-o', dest='output_folder', required=True, help='Folder to store the models')
args = argument_parser.parse_args()
#Load list of files
training_files = []
fd = open(args.inputfile,'r')
for line in fd:
if line[0]!='#':
training_files.append(line.strip())
fd.close()
print 'Total training files: %d' % len(training_files)
my_polarity_classifier = PolarityClassifier('nl')
my_polarity_classifier.train(training_files, args.output_folder)
for e, t, h in final_triples:
print(' ==>', file=sys.stderr)
print(' Expression:', e.to_line(), file=sys.stderr)
if t is None:
print(' Target: NONE', file=sys.stderr)
else:
print(' Target:', t.to_line(), file=sys.stderr)
if h is None:
print(' Holder: NONE', file=sys.stderr)
else:
print(' Holder:', h.to_line(), file=sys.stderr)
#Remove feature_file feature_file
#Remove also the target file target_features_file
os.remove(feature_file)
os.remove(target_features_file)
os.remove(holder_features_file)
## CREATE THE KAF/NAF OPINIONS
add_opinions(final_triples, kaf_naf_obj)
if args.polarity:
my_polarity_classifier = PolarityClassifier(language)
my_polarity_classifier.load_models(
os.path.join(__here__, 'polarity_models', language))
my_polarity_classifier.classify_kaf_naf_object(kaf_naf_obj)
kaf_naf_obj.dump()
from __future__ import print_function
import sys
from polarity_classifier import PolarityClassifier
from KafNafParserPy import KafNafParser
if __name__ == '__main__':
files = []
fd = open('nl.list.test')
for line in fd:
files.append(line.strip())
fd.close()
my_polarity_classifier = PolarityClassifier('nl')
my_polarity_classifier.load_models(sys.argv[1])
OK = WR = 1
for example_file in files:
this_obj = KafNafParser(example_file)
my_polarity_classifier.classify_kaf_naf_object(this_obj)
this_obj.dump()
break
GOLD = {}
list_ids_term_ids = []
for opinion in this_obj.get_opinions():