阿葛廷

人一定要靠自己!

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application of word2vec

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Clustering

  • 在字詞向量化後相似字詞之間距離較相近
    • 將相似的詞做分群(Clustering)

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application of wrod2vec / clustering code

  • load data
from gensim.models import word2vec
from sklearn.cluster import KMeans
import numpy as np
import pandas as pd
import os

## turn back to main directory
os.chdir("../")
os.getcwd()

## load word2vec model (由 part1 03_word2vec_build.ipynb 產生)
model = word2vec.Word2Vec.load('word2vec_model/CBOW')
  • similarity: 找類相關向量的詞
## get most similarity with given words
## 可以替換關鍵字
model.wv.most_similar('KMT')

'''
[('kmt', 0.6530617475509644),
 ('DPP', 0.63388592004776),
 ('國民黨', 0.6249421834945679),
 ('dpp', 0.6076689958572388),
 ('民進黨', 0.5751814246177673),
 ('政黨', 0.5520358085632324),
 ('在野黨', 0.5172843933105469),
 ('兩黨', 0.5155266523361206),
 ('白色恐怖', 0.5136880874633789),
 ('執政', 0.5134204626083374)]
'''

  • 找文字間，類相關文字

    • ex: KMT 和 綠吱，的關係相關文字，就像 DPP ?
## get most similarity with given words's relationship
## 可以替換關鍵字
model.wv.most_similar(positive=['KMT', '綠吱'], negative=['DPP'])


'''
[('周處', 0.43969738483428955),
 ('八旗', 0.40904492139816284),
 ('包壺', 0.40349867939949036),
 ('紂王', 0.4023209512233734),
 ('鼠輩', 0.3923807740211487),
 ('賣國賊', 0.3919026255607605),
 ('明末', 0.3907436430454254),
 ('三姓', 0.38343679904937744),
 ('先祖', 0.37808701395988464),
 ('異端', 0.3770984411239624)]
'''

clustering

  • 建立 dictionary

    • 做關鍵字的篩選
## create a dictionary: words as key ; count as values
words = {word: vocab.count for word, vocab in model.wv.vocab.items()}
  • 找前 10000 的關鍵字
## sort and select the top 10000 count of words
words = sorted(words.items(), key=lambda x: x[1], reverse=True)
words = words[:10000]
words = np.array(words)[:, 0]
words

'''
array(['人', '八卦', '有沒有', ..., '唯有', '考到', '派遣'], dtype='<U20')
'''

## extract the word vectors 
vecs = model.wv[words]

## run clustering algorithm
kmeans = KMeans(n_clusters=50)
cluster = kmeans.fit_predict(vecs)

## print the result
df = pd.DataFrame([words.tolist(), cluster.tolist()], index=['words', 'no. cluster']).T
df.head(n=5)

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## print every cluster of words
data = pd.concat([d['words'].reset_index(drop=True).rename(columns={0: k}) for k, d in df.groupby('no. cluster')], axis=1)
data

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document vector

  • 詞向量轉成文本向量

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document vector code

  • load data
import pickle
from gensim.models import Doc2Vec, doc2vec
from gensim.models import word2vec
import random
import numpy as np
import logging
import os

## turn back to main directory
os.chdir("../")
os.getcwd()

logging.basicConfig(format='%(asctime)s: %(levelname)s: %(message)s')
logging.root.setLevel(level=logging.INFO)

## load 'article_cutted'
with open('article_cutted', 'rb') as file:
    data = pickle.load(file)
  • average word vec
## load word2vec model
model = word2vec.Word2Vec.load('word2vec_model/CBOW')

## fiter words that not in word2vec's vocab
data_filtered = [ [w for w in l if w in model.wv] for l in data ]

## compute average word vector
avg_vector = []

## 計算每一個文件的平均 word2vec 並存在 list 裏
for l in data_filtered:
    if len(l) == 0:
        avg_vector.append(np.array([0] * 256))
    else:
        avg_vector.append(np.mean([model.wv[w] for w in l], axis =0))
  • doc2vec
sentence_list = []

for i, l in enumerate(data):
    sentence_list.append(doc2vec.LabeledSentence(words=1, tags=[str(i)]))

## print result
sentence_list[0]

'''
LabeledSentence(words=['韓瑜', '協志', '前妻', '正', '女演員', '周子', '瑜', 'TWICE', '團裡裡面', '台灣', '人', '正', '兩個', '要當', '鄉民', '老婆', '選', '五樓', '真', '勇氣'], tags=['0'])
'''

## define 轉換器
## 出現太少次數 拿掉!

model = Doc2Vec(size=256, min_count=5, window=4)

## build vocabulary
model.build_vocab(sentence_list)

# train word2vec model ; shuffle data every epoch
for i in range(20):
    random.shuffle(sentence_list)
    model.train(sentence_list, total_examples=len(data), epochs=1)

## print result
model.docvecs['0']

## save result
model.save('word2vec_model/doc2vec')

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