使用训练好的word2vector进行文本聚类

落日映苍穹つ 2021-12-09 05:09 319阅读 0赞

尝试了使用词频的词表征进行kmeans，效果不好，所以考虑看看使用word2vec的词表征会有什么不同。

1.加载word2vec

import gensim  
model = gensim.models.KeyedVectors.load\_word2vec\_format('word2vector.bigram-char')

文件是网上下载的，使用百度百科语料训练的300维词向量，看下效果：

model.most\_similar(\['男人'\])

\[('女人', 0.874478816986084),  
('老男人', 0.7225901484489441),  
('大男人', 0.7179129123687744),  
('女孩', 0.6780898571014404),  
('臭男人', 0.6778838038444519),  
('中年男人', 0.6763597726821899),  
('男孩', 0.6762259006500244),  
('真男人', 0.6674383878707886),  
('好男人', 0.6661351919174194),  
('单身男人', 0.6624549031257629)\]

len(model.vocab)：635974

2.词嵌入

将我们自己的语料（3万左右新闻数据且抽取了关键词）嵌入word2vec词向量：

#词向量嵌入
    from datetime import datetime
    import numpy as np
    start = datetime.now()
    embedding = []
    
    for idx, line in enumerate(keywords):
        vector = np.zeros(300)
        
        for word in line:
            
            if word not in model.vocab:
                vector += np.zeros(300)
            else:
                vector += model[word]
        embedding.append(vector/20)
        if (idx%100==0):
            print(idx)
        
    end = datetime.now() 
    end-start

因为我每篇文本取20个词，所以将所有词的vector/20取了个均值作为文本的向量

3.使用sklearn的kmeans进行聚类

from sklearn.cluster import KMeans
    
    kmeans = KMeans(n_clusters=100, random_state=1).fit(embedding)
    y_pred = kmeans.labels_
    cluster_center = kmeans.cluster_centers_

from collections import Counter
    
    center_dict = Counter(y_pred)
    center_dict

查看各个类别的数量

Counter({16: 314,
             27: 384,
             21: 160,
             30: 370,
             99: 223,
             15: 158,
             36: 882,
             48: 180,
             14: 184,
             43: 447,
             98: 726,
             88: 601,
             52: 195,
             53: 351,
             13: 565,
             5: 523,
             22: 417,
             23: 365,
             71: 604,
             37: 740,
             63: 355,
             29: 492,
             25: 554,
             82: 335,
             50: 727,
             41: 676,
             47: 344,
             4: 141,
             70: 274,
             12: 559,
             78: 481,
             84: 820,
             40: 237,
             75: 340,
             3: 394,
             10: 574,
             56: 564,
             59: 414,
             51: 301,
             73: 503,
             6: 560,
             60: 268,
             86: 405,
             2: 611,
             28: 485,
             66: 489,
             76: 334,
             77: 296,
             33: 226,
             65: 464,
             97: 501,
             18: 188,
             7: 218,
             54: 251,
             35: 511,
             92: 404,
             19: 454,
             74: 228,
             67: 325,
             49: 591,
             24: 306,
             69: 547,
             72: 330,
             11: 280,
             95: 374,
             81: 464,
             58: 636,
             32: 274,
             79: 115,
             87: 205,
             62: 425,
             34: 281,
             38: 330,
             96: 269,
             64: 445,
             68: 416,
             9: 382,
             91: 113,
             80: 251,
             20: 517,
             44: 264,
             93: 276,
             26: 240,
             17: 381,
             55: 129,
             57: 470,
             0: 501,
             83: 167,
             8: 261,
             89: 134,
             85: 69,
             31: 200,
             90: 147,
             46: 188,
             94: 492,
             1: 91,
             42: 401,
             45: 124,
             61: 189,
             39: 91})

看起来数量挺平均的，随便拿出来一类看看文章的标题，发现有的好有的坏，总体效果还行。

本来还想用DBSCAN算法，发现时间太久，如果跑需要将vector维度降低，所以就算了。

转载于:https://www.cnblogs.com/danny92/p/10877278.html