def __init__(self, training_data_file, classifier_dump_file):
# Instantiate classifier helper
self.helper = classifier_helper.ClassifierHelper()
self.training_data_file = training_data_file
self.classifier_dump_file = classifier_dump_file
self.test_tweet_items = []
def __init__(self, data, keyword, time, trainingDataFile, classifierDumpFile, trainingRequired = 0):
#Instantiate classifier helper
self.helper = classifier_helper.ClassifierHelper('data/feature_list.txt')
self.lenTweets = len(data)
self.origTweets = self.getUniqData(data)
self.tweets = self.getProcessedTweets(self.origTweets)
self.results = {}
self.neut_count = [0] * self.lenTweets
self.pos_count = [0] * self.lenTweets
self.neg_count = [0] * self.lenTweets
self.time = time
self.keyword = keyword
self.html = html_helper.HTMLHelper()
self.trainingDataFile = trainingDataFile
#call training model
if(trainingRequired):
self.classifier = self.getMaxEntTrainedClassifer(trainingDataFile, classifierDumpFile)
else:
f1 = open(classifierDumpFile)
if(f1):
self.classifier = pickle.load(f1)
f1.close()
else:
self.classifier = self.getMaxEntTrainedClassifer(trainingDataFile, classifierDumpFile)
def __init__(self,
data,
keyword,
time,
trainingDataFile,
classifierDumpFile,
trainingRequired=0):
#Instantiate classifier helper
self.helper = classifier_helper.ClassifierHelper(
'data/feature_list.txt')
self.lenTweets = len(data)
self.origTweets = self.getUniqData(data)
self.tweets = self.getProcessedTweets(self.origTweets)
self.results = {}
self.neut_count = [0] * self.lenTweets
self.pos_count = [0] * self.lenTweets
self.neg_count = [0] * self.lenTweets
self.trainingDataFile = trainingDataFile
self.time = time
self.keyword = keyword
#call training model
if (trainingRequired):
self.classifier = self.getSVMTrainedClassifer(
trainingDataFile, classifierDumpFile)
else:
fp = open(classifierDumpFile, 'r')
if (fp):
self.classifier = svm_load_model(classifierDumpFile)
else:
self.classifier = self.getSVMTrainedClassifer(
trainingDataFile, classifierDumpFile)
def __init__(self, data, trainingDataFile):
#Instantiate classifier helper
self.helper = classifier_helper.ClassifierHelper('myfeaturelist3.txt')
self.lenTweets = len(data)
self.origTweets = self.getUniqData(data)
self.tweets = self.getProcessedTweets(self.origTweets)
self.trainingDataFile = trainingDataFile
self.results = {}
self.neut_count = [0] * self.lenTweets
self.pos_count = [0] * self.lenTweets
self.neg_count = [0] * self.lenTweets
self.classifier = self.getSVMTrainedClassifer(trainingDataFile)
def __init__(self, data, keyword, time):
# Instantiate classifier helper
self.helper = classifier_helper.ClassifierHelper('feature_list.txt')
# Remove duplicates
self.lenTweets = len(data)
self.origTweets = self.getUniqData(data)
self.tweets = self.getProcessedTweets(self.origTweets)
self.results = {}
self.neut_count = [0] * self.lenTweets
self.pos_count = [0] * self.lenTweets
self.neg_count = [0] * self.lenTweets
self.time = time
self.keyword = keyword
self.html = html_helper.HTMLHelper()
