Question

import gensim
from gensim.models.doc2vec import TaggedDocument
taggeddocs = []
tag2tweetmap = {}
for index,i in enumerate(cleaned_tweets):
    if len(i) > 2: # Non empty tweets
        tag = u'SENT_{:d}'.format(index)
        sentence = TaggedDocument(words=gensim.utils.to_unicode(i).split(), tags=[tag])
        tag2tweetmap[tag] = i
        taggeddocs.append(sentence)
model = gensim.models.Doc2Vec(taggeddocs, dm=0, alpha=0.025, size=20, min_alpha=0.025, min_count=0)
for epoch in range(60):
    if epoch % 20 == 0:
        print('Now training epoch %s' % epoch)
   model.train(taggeddocs,total_examples=model.corpus_count,epochs=model.iter)
   model.alpha -= 0.002  
   model.min_alpha = model.alpha  
from sklearn.cluster import KMeans
dataSet = model.syn0
kmeansClustering = KMeans(n_clusters=6)
centroidIndx = kmeansClustering.fit_predict(dataSet)
topic2wordsmap = {}
for i, val in enumerate(dataSet):
    tag = model.docvecs.index_to_doctag(i)
    topic = centroidIndx[i]
    if topic in topic2wordsmap.keys():
        for w in (tag2tweetmap[tag].split()):
             topic2wordsmap[topic].append(w)
        else:
            topic2wordsmap[topic] = []
for i in topic2wordsmap:
    words = topic2wordsmap[i]
    print("Topic {} has words {}".format(i, words[:5]))

所以我试图使用doc2vec方法找出最常用的单词和主题列表。它是属性错误，说＆＃34; Doc2Vec没有属性syn0＆＃34;，我不知道如何处理它。

Answer 1

我发现这个doc2vec教程可能会给你一些关于你的问题的线索。

https://medium.com/@mishra.thedeepak/doc2vec-simple-implementation-example-df2afbbfbad5

＆＃39; Doc2Vec＆＃39;对象没有属性＆＃39; syn0＆＃39;

1 个答案: