相似度分析

import re
import jieba

doc1 = ‘‘‘曝甜瓜和篮网球员一起训练 新赛季有望加入篮网?：自从上赛季在火箭经历了失败后，安东尼一直无人问津，今年夏天关于他打不上球的话题也是被反复议论，根据最新消息，安东尼最近正和篮网球员一起训练。
‘‘‘
doc2 = ‘‘‘周琦团队:感谢新疆耐心等待 新疆仍支持周琦留洋：昨天，男篮国手周琦重返新疆男篮的事尘埃落定。此后，周琦团队很快发表声明，新疆男篮仍会支持周琦挑战高水平联赛，而周琦之所以没有最终到欧洲去打球，是因为兼顾国家队备战，无法抽身去参加试训。
‘‘‘
doc3 = ‘‘‘官宣!新疆宣布周琦回归 新赛季向总冠军发起冲击：北京时间8月13日，新疆篮球俱乐部官宣已经与周琦完成签约。周琦将重新身披新疆队战袍，征战CBA赛场。新赛季周琦与球队向总冠军发起冲击。
‘‘‘
doc4 = ‘‘‘官宣!新疆宣布周琦回归 新赛季向总冠军发起冲击‘‘‘
doc5 = ‘‘‘欧联-武磊替补登场 西班牙人2-2总比分5-3进正赛_手机搜狐网,阵容方面，西班牙人主帅加耶戈排出4-3-3阵型，卡莱罗搭档路易斯-洛佩斯出任中卫，达德尔、罗卡、格拉内罗组成‘‘‘
doc_complete = [doc1, doc2, doc3]
# doc_clean = [clean(doc).split() for doc in doc_complete]
def fenci(title):
    corpus = ‘‘
    corpus = title
    stopwords = [line.strip() for line in open(‘../data/stopWord.txt‘, ‘r‘, encoding=‘utf-8‘).readlines()]
    r4 = "\\【.*?】+|\\《.*?》+|\\#.*?#+|[0-9]+|[.!/_,$&%^*()<>+""‘?@|:~{}#]+|[——！\\\，。=？、：“”‘’￥……（）《》【】]"
    BIFEN_RE = re.compile(r‘[0-9]+[:|：|-|-|+]\s{0,5}[0-9]+‘)

    doc = re.sub(BIFEN_RE, ‘比分‘, corpus)
    doc = re.sub(r‘VS‘, ‘对战‘, doc, flags=re.IGNORECASE)
    doc = re.sub(r‘8强‘, ‘八强‘, doc)
    doc = re.sub(r‘4强‘, ‘四强‘, doc)

    doc = re.sub(r‘16强‘, ‘十六强‘, doc)
    doc = re.sub(r4, ‘ ‘, doc)

    jieba.load_userdict("../data/user_dict.txt")
    seg = jieba.cut(doc)
    result = ‘‘
    for word in seg:
        if word not in stopwords:
            result += word + ‘ ‘
    return result

doc_clean = [fenci(doc).split() for doc in doc_complete]
# print(doc_clean)
# LDA
import gensim
from gensim import corpora

# Creating the term dictionary of our courpus, where every unique term is assigned an index.
dictionary = corpora.Dictionary(doc_clean)

# Converting list of documents (corpus) into Document Term Matrix using dictionary prepared above.
doc_term_matrix = [dictionary.doc2bow(doc) for doc in doc_clean]
Lda = gensim.models.ldamodel.LdaModel

# Running and Trainign LDA model on the document term matrix.
ldamodel = Lda(doc_term_matrix, num_topics=3, id2word = dictionary, passes=50)
print(ldamodel.print_topics(num_topics=3, num_words=5))

# DF
for doc in doc_clean:
    # dic = dict(zip(doc, [(i-i) for i in range(len(doc))]))
    # print(dic)
    dic = {}
    for word in doc:
        if dic. __contains__(word):
            dic[word]  += 1
        else:
            dic[word] = 1
    print(dic)
    l = sorted(dic.items(), key=lambda d:d[1],reverse=True)
    print(l)


import jieba
import jieba.analyse

keywords = jieba.analyse.extract_tags(doc3, topK=5, withWeight=True, allowPOS=(‘n‘, ‘nr‘, ‘ns‘))

# print(type(keywords))
# <class ‘list‘>

for item in keywords:
    print(item[0], item[1])

print(‘****************‘)
keywords = jieba.analyse.textrank(doc3, topK=5, withWeight=True, allowPOS=(‘n‘, ‘nr‘, ‘ns‘))

# type(keywords)
# <class ‘list‘>

for item in keywords:
    print(item[0], item[1])

from gensim.models import word2vec
import jieba
import re
import numpy as np

# 利用word2vec计算

model = word2vec.Word2Vec.load(‘../model/last.model‘)
def fenci(title):
    corpus = ‘‘
    corpus = title
    r4 = "\\【.*?】+|\\《.*?》+|\\#.*?#+|[.!/_,$&%^*()<>+""‘?@|:~{}#]+|[——！\\\，。=？、：“”‘’￥……（）《》【】]"
    BIFEN_RE = re.compile(r‘[0-9]+[:|：|-|-|+]\s{0,5}[0-9]+‘)
    doc = re.sub(r4, ‘ ‘, corpus)
    doc = re.sub(BIFEN_RE, ‘比分‘, doc)
    doc = re.sub(r‘VS‘, ‘对战‘, doc, flags=re.IGNORECASE)
    doc = re.sub(r‘8强‘, ‘八强‘, doc)
    doc = re.sub(r‘4强‘, ‘四强‘, doc)

    doc = re.sub(r‘16强‘, ‘十六强‘, doc)

    jieba.load_userdict("../data/user_dict.txt")
    seg = jieba.cut(doc)
    result = ‘‘
    for word in seg:
        result += word + ‘ ‘
    return result

sent1 = u‘欧联-武磊替补登场 西班牙人2-2总比分5-3进正赛_手机搜狐网‘
sent2 = u‘武磊替补登场‘

def getsentecevec(sentence):
    vec = np.zeros(100)
    # print(vec)
    count = 0
    model = word2vec.Word2Vec.load(‘../model/last.model‘)
    for wor in fenci(sentence):
        try:
            vec += model.wv[wor]
            count += 1
        except:
            # print(wor + ‘not in voca‘)
            pass
    vec /= count
    return vec
def xiangsidu(vec_a,vec_b):
    a = np.array(vec_a)
    b = np.array(vec_b)

    ret = np.sum(a * b) / (np.sqrt(np.sum(a ** 2)) * np.sqrt(np.sum(b ** 2)))
    print(‘分数:‘ + str(ret))

doc1 = u‘欧联-武磊替补登场 西班牙人2-2总比分5-3进正赛_手机搜狐网‘
doc2 = u‘西媒盛赞武磊:没有让人失望!跑位很出色 踢法灵活本报讯（记者王帆）‘
doc3 = u‘西班牙人前瞻：武磊有望重回首发 伤员悉数归队_手机搜狐网‘
doc4 = u‘表现平平！武磊仅获西媒评5分 0射门进攻端无贡献_手机搜狐网‘
doc = u‘武磊替补登场‘
str5 = "篮球贡献获世界肯定 姚明将入选NBA名人堂 晚间体育新闻"
str6 = "姚明已确定入选2016届名人堂 午间体育新闻"
str7 = "篮球贡献获世界肯定 姚明将入选NBA名人堂"
str8 = "美媒：篮球明星姚明将入选美国NBA名人堂"
str9 = "姚明已确定入选2016届名人堂"
str1 = "[冠军欧洲]特别企划：狭路相逢 球队交锋史"
str2 = "[冠军欧洲]特别企划：逆转之态 欧冠小百科"
str3 = "[冠军欧洲]特别企划：逆转之态 姚明小百科"

doc_c = [str5, str6, str7, str8, str2, doc1,doc2,str3,doc4]
vec_a = getsentecevec(str9)
for d in doc_c:
    vec_b = getsentecevec(d)
    xiangsidu(vec_a, vec_b)

# sent1 = u‘军训服蹲下崩线‘
# sent2 = u‘军训服蹲下就崩线‘
# sent1 = u‘吴亦凡女友身份 ‘
# sent2 = u‘吴亦凡女友疑曝光‘