1 def word_vector_gener():
 2     """
 3     幾種不同的方法來生成詞向量
 4     :return:
 5     """
 6     from gensim.models import Word2Vec
 7     from gensim.test.utils import common_texts
 8     # 1.word2vec
 9     # 獲取原始資料
10     DATA_PATH = './word2vec_data.txt'
11     word2evctor = open('./word2vector.txt', '
 
w', encoding='utf8')
12     word_list = []
13     finall = []
14     # jieba分詞
15     with open(DATA_PATH, 'r', encoding='utf8') as file:
16         for each_line in file.readlines():
17             # 分詞
18             cut_word = list(jieba.cut(each_line.strip()))
19             # 去停用詞
20             stopwords = [w.strip() for
 
 w in open('./stop_words.txt', 'r', encoding='utf8')]
21             temp = []
22             for each in cut_word:
23                 if each not in stopwords and each.strip():
24                     temp.append(each)
25                     word_list.append(each)
26             finall.append(temp)
27
 
     # 訓練模型
28     model = Word2Vec(finall, size=100, window=1, min_count=1, workers=4)
29     model.save('./word2vec_model.')
30     # 檢視詞向量
31     for word in list(set(word_list)):
32         content = str(word) + '\t' + str(model[word])
33         word2evctor.write(content+'\n')
34         print(content)
35 
36 
37     print('ok')
38 
39 
40 if __name__ == '__main__':
41     word_vector_gener()