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()