def __init__(self):
data = GetDataLocation()
self.data_path = data.get_path()
self.pos_lines = open(os.path.join(self.data_path, "rt-polarity.pos"), "r").read().splitlines()
self.neg_lines = open(os.path.join(self.data_path, "rt-polarity.neg"), "r").read().splitlines()
# print num_lines_pos #total positive lines
# print num_lines_neg #total neg lines
pos_count = open(os.path.join(self.data_path, "rt-polarity.pos"), "r").read().split()
neg_count = open(os.path.join(self.data_path, "rt-polarity.neg"), "r").read().split()
self.pos_word_count = len(pos_count)
self.neg_word_count = len(neg_count)
self.trainset = [(x, 1) for x in self.pos_lines] + [(x, -1) for x in self.neg_lines]
self.pos_1gram = {}
self.pos_2gram = {}
self.pos_3gram = {}
self.pos_4gram = {}
self.pos_4gram = {}
self.neg_1gram = {}
self.neg_2gram = {}
self.neg_3gram = {}
self.neg_4gram = {}
self.neg_5gram = {}
self.train()
def __init__(self, n):
data = GetDataLocation()
self.data_path = data.get_path()
self.symbols = ['!', '@', '#', '$', '%', '^', '&', '*', '(', ')', '-', '_', '+', '=', ',', '.',
'<', '>', '?', '/']
#train set data statistic
#define positive and negative word training set
#train set data statistic
#positive word training set
poslines = open(self.data_path+'/rt-polarity.pos', 'r').read().splitlines()
#negative word training set
neglines = open(self.data_path+'/rt-polarity.neg', 'r').read().splitlines()
self.stop_list=open(self.data_path+'/stopwordslist_1.file','r').read().splitlines()
#test_data=open(r'pos.txt','r').read().splitlines()
self.poslinesTrain = poslines[:]
self.neglinesTrain = neglines[:]
#open the file for checking the emotions
self.pos_smiley = open(self.data_path+'/positive_smile.pos', 'r').read().split()
self.neg_smiley = open(self.data_path+'/negative_smile.neg', 'r').read().split()
#create the train set and the test set by attaching labels to text to form a
#list of tuples (sentence, label). Labels are 1 for positive, -1 for negative
self.trainset = [(x, 1) for x in self.poslinesTrain] + [(x, -1) for x in self.neglinesTrain]
self.poswords = {}#for storing the word count in unigram
self.negwords = {}
self.poswords_bigram = {}#for bigram
self.negwords_bigram = {}
self.poswords_ngram = {}#for n gram
self.negwords_ngram = {}
self.n = n
self.train(self.n)
def __init__(self):
data = GetDataLocation()
path = data.get_path()
file_name = os.path.join(path, "spellcheck")
self.NWORDS = self.train(self.words(file(file_name).read()))
self.alphabet = 'abcdefghijklmnopqrstuvwxyz'
self.pos = []
self.neg = []
return
def __init__(self):
self.training_percent = 100 # in 0 to 100 range
self.testing_percent = 20 # in 0 to 100 range
data = GetDataLocation()
data_path = data.get_path()
self.train_file = data_path + "/svm.train"
self.test_file = data_path + "/svm.test"
self.model_file = data_path + "/model/svm.modeluni"
self.dict_file = data_path + "/svm.dictuni"
self.pos_file = data_path + "/svm.pos"
self.neg_file = data_path + "/svm.neg"
self.stop_words_file = data_path + "/stop"
def __init__(self):
data = GetDataLocation()
path = data.get_path()
file_name = os.path.join(path, "stopwords")
self.stop_word_list = open(file_name).read().splitlines()
return
print "Classification Done!"
file = open("result", 'a')
file.write(param + ";" + str(p_acc) + '\n')
file.close()
except:
file = open("result", 'a')
file.write(param + ";ERROR\n")
file.close()
continue
settings = Settings()
data = GetDataLocation()
data_path = data.get_path()
settings.training_percent = 80 # in 0 to 100 range
settings.testing_percent = 20 # in 0 to 100 range
settings.train_file = data_path + "/svm.train"
settings.test_file = data_path + "/svm.test"
settings.model_file = data_path + "/model/svm.modeluni"
settings.dict_file = data_path + "/svm.dictuni"
settings.pos_file = data_path + "/svm.pos"
settings.neg_file = data_path + "/svm.neg"
settings.stop_words_file = data_path + "/stop"
run = Analysis(settings)
#run = Run(settings, False)
