def get_instances(self, label_file, xml_file):
instances = []
labels_final = set()
tagger = PerceptronTagger(
) # load nltk perceptron just once to speed up tagging
labels_dict = {
0: "anger",
1: "disgust",
2: "fear",
3: "joy",
4: "sadness",
5: "surprise"
}
tree = ET.parse(xml_file)
root = tree.getroot()
with open(label_file) as f:
for sent, line in izip(root, f):
id_xml = sent.attrib.values()[0]
id_labels = line.rstrip().split()
id_file = id_labels[0]
if id_xml == id_file:
for i in sent.itertext():
text = i
labels = id_labels[1:]
label = labels.index(
str(max([int(label) for label in labels])))
inst = Instance(text, labels_dict[label])
inst_tokenized = word_tokenize(text)
inst_tagged = tagger.tag(inst_tokenized)
for tokentag in inst_tagged:
token = Token(tokentag[0], tokentag[1])
inst.add_token(token)
instances.append(inst)
labels_final.add(label)
return instances, labels_final
def get_instances(self, folder):
# happiness/joy???????????????????????????
labels_dict = {
"hp": "joy",
"sd": "sadness",
"ag": "anger",
"dg": "disgust",
"sp": "surprise",
"fr": "fear"
}
instances = []
labels = set()
tagger = PerceptronTagger(
) # load nltk perceptron just once to speed up tagging
with open(folder) as f:
for line in f:
label, id, text = line.strip().split(
" ", 2) # split by first two spaces only
if label == "ne": # ignore no emotion
continue
inst = Instance(text, labels_dict[label])
inst_tokenized = word_tokenize(text)
inst_tagged = tagger.tag(inst_tokenized)
for tokentag in inst_tagged:
token = Token(tokentag[0], tokentag[1])
inst.add_token(token)
instances.append(inst)
labels.add(label)
return instances, labels
def get_instances(self, folder):
instances = []
labels = set()
for author in os.listdir(folder):
path = folder + "/" + author + "/agree-sent/"
path_pos = folder + "/" + author + "/pos/"
if os.path.exists(path) and os.path.exists(path_pos):
for af in os.listdir(path):
current = os.path.join(path, af)
current_pos = os.path.join(
path_pos,
af.split('.')[0] + '.sent.okpuncs.props.pos')
if os.path.isfile(current) and os.path.isfile(current_pos):
agree_data = open(current, "rb")
pos_data = open(current_pos, "rb").readlines()
for x in agree_data:
x = x.strip()
id = int(x.split("@")[0])
y = pos_data[id].strip()
label = int(x.split("@")[1])
text = x.split("@")[2]
inst = Instance(text, label)
for tagtoken in y.split("):("):
tag = tagtoken.split(" ")[0].lstrip("(")
token = tagtoken.split(" ")[1]
token = Token(token, tag)
inst.add_token(token)
instances.append(inst)
labels.add(label)
return instances, labels
def get_instances(self, folder):
instances = []
labels = set()
tagger = PerceptronTagger() # load nltk perceptron just once to speed up tagging
with io.open(folder, encoding="utf-8") as f:
# with open(folder) as f:
for line in f:
# line = unicode(line).encode("utf-8")
line_split = line.rstrip().split("\t")
if len(line_split) != 3:
continue
id, text, label = line_split
id = id.rstrip(":")
text = re.sub('[#]', '', text.rstrip())
label = re.sub('[^a-z]', '', label)
inst = Instance(text, label)
inst_tokenized = word_tokenize(text)
inst_tagged = tagger.tag(inst_tokenized)
for tokentag in inst_tagged:
token = Token(tokentag[0], tokentag[1])
inst.add_token(token)
instances.append(inst)
labels.add(label)
return instances, labels
