def bare_dict():
negations = set(json.load(open(os.path.join(DATA_DIR, 'negations.json'))))
with open(os.path.join(DATA_DIR, "degrees.json")) as f:
degrees = json.load(f)
with open(os.path.join(DATA_DIR, 'pos.txt')) as f:
pos_emotion = set([x.strip() for x in f.readlines()])
with open(os.path.join(DATA_DIR, 'neg.txt')) as f:
neg_emotion = set([x.strip() for x in f.readlines()])
# with open(os.path.join(DATA_DIR, 'pos_eva.txt')) as f:
# pos_envalute = set([x.strip() for x in f.readlines()])
# with open(os.path.join(DATA_DIR, 'neg_eva.txt')) as f:
# neg_envalute = set([x.strip() for x in f.readlines()])
# places = os.path.join(os.path.dirname(__file__), "../dictionaries/places.txt")
# tokenizer.load_userdict(places)
# with open(os.path.join(DATA_DIR, 'pos_sentence.txt')) as f1,\
# open(os.path.join(DATA_DIR, 'neg_sentence.txt')) as f2:
# s1 = set([x.strip() for x in f1.readlines()])
# s2 = set([x.strip() for x in f2.readlines()])
# pos_emotion.union(s1)
# neg_emotion.union(s2)
pos_neg = pos_emotion.union(neg_emotion)
# pos_neg_eva = pos_envalute.union(neg_envalute)
tokenizer.load_userdict(pos_neg)
def main():
jieba_instance = Tokenizer()
seg_list = jieba_instance.cut("我来到北京清华大学", cut_all=True)
print(type(seg_list))
print("Full Mode: " + "/ ".join(seg_list)) # 全模式
seg_list = jieba_instance.cut("他来到了网易杭研大厦") # 默认是精确模式
print(", ".join(seg_list))
seg_list = jieba_instance.cut_for_search(
"小明硕士毕业于中国科学院计算所，后在日本京都大学深造") # 搜索引擎模式
print(", ".join(seg_list))
t1 = datetime.datetime.now()
initialize()
t2 = datetime.datetime.now()
print("initialize costs:%s" % (t2 - t1))
print(lcut("我来到北京清华大学"))
print(list(cut("我来到北京清华大学")))
print(cut("我来到北京清华大学", cut_all=True))
print(lcut_for_search("我来到北京清华大学"))
print(list(cut_for_search("我来到北京清华大学")))
print(pseg.lcut("我来到北京清华大学"))
print(list(pseg.cut("我来到北京清华大学")))
s = "此外，公司拟对全资子公司吉林欧亚置业有限公司增资4.3亿元，增资后，吉林欧亚置业注册资本由7000万元增加到5亿元。吉林欧亚置业主要经营范围为房地产开发及百货零售等业务。目前在建吉林欧亚城市商业综合体项目。2013年，实现营业收入0万元，实现净利润-139.13万元。"
r = analyse.extract_tags(s)
print(r)
r = analyse.textrank(s, withWeight=True)
print(r)
tr = TextRank(jieba_instance)
print(tr.textrank(s, topK=2, withWeight=True))
tf = TFIDF(jieba_instance)
print(tf.extract_tags(s, topK=10))
result = jieba_instance.tokenize('永和服装饰品有限公司')
for tk in result:
print("word %s\t\t start: %d \t\t end:%d" % (tk[0], tk[1], tk[2]))
print(tokenize('永和服装饰品有限公司', mode="search"))
jieba_instance.load_userdict(["卧槽"])
load_userdict(set(["卧槽"]))
def accept_set_as_arg(self):
jieba.load_userdict(set([]))
def accept_list_as_arg(self):
jieba.load_userdict([])
def accept_string_as_arg(self):
jieba.load_userdict("")