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 setUpClass(cls):
cls.dt = Tokenizer(DICT)
cls.sentence = "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。"
cls.extractor = TextRankExtractor(cls.dt)
def takes_arg3_as_stopword_path(self):
Tokenizer(DICT, USER_DICT, STOP_WORD)
def takes_arg2_as_user_dict_path(self):
Tokenizer(DICT, USER_DICT)
def setUpClass(cls):
cls.dt = Tokenizer(DICT)
cls.dt.add_word("区块链", 10, "nz")
# -*- coding: utf-8 -*-
import re
import os
import math
import pickle
from collections import Counter
from cppjieba_py import Tokenizer
big_dict = os.path.join(os.path.dirname(__file__), "data", "dict.txt.big")
tokenizer = Tokenizer(big_dict)
DATA_PATH = os.path.join(os.path.dirname(__file__), "data", "dict.pkl")
FIXED_PA = 1.6
def load_data():
with open(DATA_PATH, "rb") as f:
return pickle.load(f)
class Classifier():
def __init__(self, *args):
self.initialized = False
if len(args):
self._initialize(*args)
def _initialize(self, pos_emotion, pos_evaluation, neg_emotion,
neg_evaluation, degrees, negations):
self.pos_emotion = pos_emotion
self.neg_emotion = neg_emotion