我如何在 LDA 中查看每个主题的所有文档？

Question

我正在使用 LDA 来了解一篇好文章的主题。我设法打印了主题，但我想打印每个包含您主题的文本。

数据：

it's very hot outside summer
there are not many flowers in winter
in the winter we eat hot food
in the summer we go to the sea
in winter we used many clothes
in summer we are on vacation
winter and summer are two seasons of the year

我尝试使用 sklearn，我可以打印主题，但我想打印属于每个主题的所有短语

from sklearn.feature_extraction.text import CountVectorizer
from sklearn.decomposition import LatentDirichletAllocation
import numpy as np
import pandas

dataset = pandas.read_csv('data.csv', encoding = 'utf-8')
comments = dataset['comments']
comments_list = comments.values.tolist()

vect = CountVectorizer()
X = vect.fit_transform(comments_list)

lda = LatentDirichletAllocation(n_topics = 2, learning_method = "batch", max_iter = 25, random_state = 0)

document_topics = lda.fit_transform(X)

sorting = np.argsort(lda.components_, axis = 1)[:, ::-1]
feature_names = np.array(vect.get_feature_names())

docs = np.argsort(comments_list[:, 1])[::-1]
for i in docs[:4]:
    print(' '.join(i) + '\n')

输出良好：

Topic 1
it's very hot outside summer
in the summer we go to the sea
in summer we are on vacation
winter and summer are two seasons of the year

Topic 2
there are not many flowers in winter
in the winter we eat hot food
in winter we used many clothes
winter and summer are two seasons of the year

Answer 1

如果要打印文档，需要指定。

print(" ".join(comments_list[i].split(",")[:2]) + "\n")

我如何在 LDA 中查看每个主题的所有文档？

How I can see all documents per topic in LDA?

python

python-3.x

scikit-learn

lda

topic-modeling