#!/usr/bin/env Python3 # coding=utf-8 import jieba jieba.load_userdict("C:\\Users\\Desktop\\s_proj\\dict.txt") #自定義分詞詞典#分詞并將結果存入txt f1 =open("C:\\Users\\Desktop\\neg.txt","r",encoding='utf-8',errors='ignore') f2 =open("C:\\Users\\Desktop\\car_fenci.txt", 'w',encoding='utf-8',errors='ignore') lines =f1.readlines() # 讀取全部內容 w='' for line in lines:line.replace('\t', '').replace('\n', '').replace(' ','')seg_list = jieba.cut(line, cut_all=False)f2.write(" ".join(seg_list)) f1.close() f2.close()from gensim.models import word2vec import logging logging.basicConfig(format='%(asctime)s:%(levelname)s: %(message)s', level=logging.INFO)#訓練word2vec模型 sentences=word2vec.Text8Corpus(u"C:\\Users\\Desktop\\car_fenci.txt") model =word2vec.Word2Vec(sentences,size=400, window=10, min_count=1) #訓練skip-gram模型，默認window=5 print (model)#保存模型 model.save("model_word") # 以一種C語言可以解析的形式存儲詞向量 model.wv.save_word2vec_format("model_word.bin", binary=True) if __name__ == "__main__": pass #打開要計算相似度的2個文本 f3 =open(r"C:\Users\Desktop\s_proj\keyword.txt","r",encoding='utf-8',errors='ignore') f4=open(r"C:\Users\Desktop\s_proj\c.txt","r",encoding='utf-8',errors='ignore')#要計算的2個文本預處理 f=[] ff=[] for i in f3.readlines():i=i.replace('\n','')f.append(i)for j in f4.readlines():j=j.replace('\n','')ff.append(j) #相似度計算 for ii in f:for jj in ff:try:y1 = model.wv.similarity(ii, jj) print (ii+' '+jj,y1) except:print (ii+' '+jj,0)

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