トピックをPythonのLDAの各トピックの上位20語のリストに変換する方法

Question

現在、私はLDAの対数をPythonで処理しています。話題を各トピックの上位20語のリストに変換したい私はコードの下で試したが、別の出力を得た。 私の出力は、次の形式で入力してください：topic=2,words=20

["(u'ngma', 0.034841332255132154)", "(u'video', 0.0073756817356584745)", "(u'youtube', 0.006524039676605746)", "(u'liked', 0.0065240394176856644)",] 
["(u'ngma', 0.024537057880333127)", "(u'photography', 0.0068263432438681482)", "(u'tvallwhite', 0.0029535361359022566)", "(u'3', 0.0029252727655122079)"] 

マイコード：

`ldamodel = Lda(doc_term_matrix, num_topics=2, id2word = dictionary,passes=50) 
lda=ldamodel.print_topics(num_topics=2, num_words=3) 

f=open('LDA.txt','w') 
f.write(str(lda)) 
f.close() 

topics_matrix = ldamodel.show_topics(formatted=False,num_words=10) 
topics_matrix = np.array((topics_matrix),dtype=list) 
topic_words = topics_matrix[:, 1] 
for i in topic_words: 
    print([str(word) for word in i]) 
    print()` 

編集-1：私は、出力の下になった

['men', 'kill', 'soldier', 'order', 'patient', 'night', 'priest', 'becom', 'new', 'speech', 'friend', 'decid', 'young', 'ward', 'state', 'front', 'would', 'home', 'two', 'father'] 

["n't", 'go', 'fight', 'doe', 'home', 'famili', 'car', 'night', 'say', 'next', 'ask', 'day', 'want', 'show', 'goe', 'friend', 'two', 'polic', 'name', 'meet'] 

topic_words = [] 
for i in range(3): 
    tt = ldamodel.get_topic_terms(i,10) 
    topic_words.append([pair[0] for pair in tt]) 
    print topic_words 

は非期待される出力の結果：

[[1897, 135, 130, 127, 70, 162, 445, 656, 608, 1019], [1897, 364, 56, 1236, 181, 172, 449, 48, 15, 18], [1897, 163, 11, 70, 166, 345, 480, 9, 60, 351]] 

Answer 1

this-

from gensim import corpora 
import gensim 
from gensim.models.ldamodel import LdaModel 
from gensim.parsing.preprocessing import STOPWORDS 

# example docs 
doc1 = """ 
Java (Indonesian: Jawa; Javanese: ꦗꦮ; Sundanese: ᮏᮝ) is an island of Indonesia.\ 
With a population of over 141 million (the island itself) or 145 million (the \ 
administrative region), Java is home to 56.7 percent of the Indonesian population \ 
and is the most populous island on Earth.[1] The Indonesian capital city, Jakarta, \ 
is located on western Java. Much of Indonesian history took place on Java. It was \ 
the center of powerful Hindu-Buddhist empires, the Islamic sultanates, and the core \ 
of the colonial Dutch East Indies. Java was also the center of the Indonesian struggle \ 
for independence during the 1930s and 1940s. Java dominates Indonesia politically, \ 
economically and culturally. 
""" 
doc2 = """ 
Hydrogen fuel is a zero-emission fuel when burned with oxygen, if one considers water \ 
not to be an emission. It often uses electrochemical cells, or combustion in internal \ 
engines, to power vehicles and electric devices. It is also used in the propulsion of \ 
spacecraft and might potentially be mass-produced and commercialized for passenger vehicles \ 
and aircraft.Hydrogen lies in the first group and first period in the periodic table, i.e. \ 
it is the first element on the periodic table, making it the lightest element. Since \ 
hydrogen gas is so light, it rises in the atmosphere and is therefore rarely found in \ 
its pure form, H2.""" 

doc3 = """ 
The giraffe (Giraffa) is a genus of African even-toed ungulate mammals, the tallest living \ 
terrestrial animals and the largest ruminants. The genus currently consists of one species, \ 
Giraffa camelopardalis, the type species. Seven other species are extinct, prehistoric \ 
species known from fossils. Taxonomic classifications of one to eight extant giraffe species\ 
have been described, based upon research into the mitochondrial and nuclear DNA, as well \ 
as morphological measurements of Giraffa, but the IUCN currently recognizes only one \ 
species with nine subspecies. 
""" 

documents = [doc1, doc2, doc3] 
document_wrd_splt = [[word for word in document.lower().split() if word not in STOPWORDS] \ 
for document in documents] 

dictionary = corpora.Dictionary(document_wrd_splt) 
print(dictionary.token2id) 

corpus = [dictionary.doc2bow(text) for text in texts] 

lda = LdaModel(corpus, num_topics=3, id2word = dictionary, passes=50) 

num_topics = 3 
topic_words = [] 
for i in range(num_topics): 
    tt = lda.get_topic_terms(i,20) 
    topic_words.append([dictionary[pair[0]] for pair in tt]) 

# output 
>>> topic_words[0] 
['indonesian', 'java', 'species', 'island', 'population', 'million', '(the', 'java.', 'center', 'giraffe', 'currently', 'genus', 'city,', 'economically', 'administrative', 'east', 'sundanese:', 'itself)', 'took', '1940s.'] 
>>> topic_words[1] 
['vehicles', 'fuel', 'hydrogen', 'periodic', 'table,', 'i.e.', 'uses', 'form,', 'considers', 'zero-emission', 'internal', 'period', 'burned', 'cells,', 'rises', 'pure', 'atmosphere', 'aircraft.hydrogen', 'water', 'engines,'] 
>>> topic_words[2] 
['giraffa,', 'even-toed', 'living', 'described,', 'camelopardalis,', 'consists', 'extinct,', 'seven', 'fossils.', 'morphological', 'terrestrial', '(giraffa)', 'dna,', 'mitochondrial', 'nuclear', 'ruminants.', 'classifications', 'species,', 'prehistoric', 'known']