文本聚類的主題建模效率低下

Question

我嘗試使用LDA進行文本聚類，但是並沒有給我獨特的聚類。 下面是我的代碼

#Import libraries
from gensim import corpora, models
import pandas as pd
from gensim.parsing.preprocessing import STOPWORDS
from itertools import chain

#stop words
stoplist = list(STOPWORDS)
new = ['education','certification','certificate','certified']
stoplist.extend(new)
stoplist.sort()

#read data
dat = pd.read_csv('D:\data_800k.csv',encoding='latin').Certi.tolist()
#remove stop words
texts = [[word for word in document.lower().split() if word not in stoplist] for document in dat]
#dictionary
dictionary = corpora.Dictionary(texts)
#corpus
corpus = [dictionary.doc2bow(text) for text in texts]
#train model
lda = models.LdaMulticore(corpus, id2word=dictionary, num_topics=25, workers=4,minimum_probability=0)
#print topics
lda.print_topics(num_topics=25, num_words=7)
#get corpus
lda_corpus = lda[corpus]
#calculate cutoff score
scores = list(chain(*[[score for topic_id,score in topic] \
                      for topic in [doc for doc in lda_corpus]]))


#threshold
threshold = sum(scores)/len(scores)
threshold
**0.039999999971137644**

#cluster1
cluster1 = [j for i,j in zip(lda_corpus,dat) if i[0][1] > threshold]

#cluster2
cluster2 = [j for i,j in zip(lda_corpus,dat) if i[1][1] > threshold]

問題是在cluster1中有重疊的元素，這些元素往往出現在cluster2中，依此類推。

我也嘗試將閾值手動增加到0.5，但這給了我同樣的問題

Answer 1

那是現實的。

通常，文檔或單詞都不能唯一地分配給單個群集。

如果您要手動標記一些數據，則還將快速找到一些不能清楚地標記為一個或另一個的文檔。 所以很好，我想算法不會假裝有一個很好的唯一分配。