Я использую LDA, чтобы узнать темы отличного текста. Мне удалось напечатать темы, но я хотел бы напечатать каждый текст с вашей темой.
Данные:
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
Я пытался использовать sklearn, и я могу распечатать темы, но я хотел бы распечатать все фразы, относящиеся к каждой теме.
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')
Хороший результат:
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
