parser.add_argument('-t', '--tweets_path',
help='tweets path',
default='data/rawdata/',
type=str)
parser.add_argument('-f', '--dest_file',
help='File where is stored the model',
default='classifier',
type=str)
args = parser.parse_args()
# Process the training of the model
print "Initialization"
s = SetManager(args.csv_path, args.tweets_path)
tokenizer = Tokenizer(preserve_case=False)
cm = ClassifierManager()
# List of the sentiments used (feature space)
sentiments = ['Virtue', 'Weak', 'HU', 'Hostile', 'EnlTot', 'ComForm',
'Passive', 'Pstv', 'Ngtv', 'PowTot', 'Strong', 'Positiv',
'IAV', 'Active', 'Negativ']
sentiments.reverse()
classifiers = {}
print "DB connexion"
con = None
try:
db, usr, pwd = load_database('database.properties')
con = psycopg2.connect(database=db, user=usr, host='localhost')
result = max(predictions.iterkeys(), key=(lambda k: predictions[k]))
if result == 'neg':
return -1
elif result == 'neut':
return 0
else:
return 1
return max(predictions.iterkeys(), key=(lambda k: predictions[k]))
# return predictions
args = parser.parse_args()
print "Initialization"
s = SetManager(args.csv_path, args.tweets_path)
tokenizer = Tokenizer(preserve_case=False)
cm = ClassifierManager()
# List of the sentiments used (feature space)
sentiments = ['Virtue', 'Weak', 'HU', 'Hostile', 'EnlTot', 'ComForm',
'Passive', 'Pstv', 'Ngtv', 'PowTot', 'Strong', 'Positiv',
'IAV', 'Active', 'Negativ']
sentiments.reverse()
classifiers = {}
print "DB connexion"
con = None
testingSet = None
try:
db, usr, pwd = load_database('database.properties')
con = psycopg2.connect(database=db, user=usr, host='localhost')