主题建模-在sklearn中运行LDA：如何计算Wordcloud？

Question

我在sklearn中训练了我的LDA模型以构建主题模型，但是不知道如何为每个获得的主题计算关键字Wordcloud？

这是我的LDA模型：

vectorizer = CountVectorizer(analyzer='word',       
                         min_df=3,                        
                         max_df=6000,
                         stop_words='english',             
                         lowercase=False,                   
                         token_pattern ='[a-zA-Z0-9]{3,}' 
                         max_features=50000,             
                        )
data_vectorized = vectorizer.fit_transform(data_lemmatized) # data_lemmatized is all my processed document text

best_lda_model = LatentDirichletAllocation(batch_size=128, doc_topic_prior=0.1,
                      evaluate_every=-1, learning_decay=0.7,
                      learning_method='online', learning_offset=10.0,
                      max_doc_update_iter=100, max_iter=10,
                      mean_change_tol=0.001, n_components=10, n_jobs=None,
                      perp_tol=0.1, random_state=None, topic_word_prior=0.1,
                      total_samples=1000000.0, verbose=0)

lda_output = best_lda_model.transform(data_vectorized)

我知道best_lda_model.components _给出了主题词的权重...vectorizer.get_feature_names（）给出每个主题中词汇表中的所有单词...

非常感谢！

Answer 1

您必须遍历模型'components_'，模型的大小为[n_components，n_features]，因此第一个维度包含主题，第二个维度包含词汇表中每个单词的分数。因此，您首先需要找到与该主题最相关的单词的索引，然后使用通过使用get_features_names（）定义的“ vocab”字典来检索这些单词。

import numpy as np

# define vocabulary to get words names 
vocab = vectorizer.get_feature_names()

# dictionary to store words for each topic and number of words per topic to retrive 
words = {}
n_top_words = 10

for topic, component in enumerate(model.components_):

    # need [::-1] to sort the array in descending order
    indices = np.argsort(component)[::-1][:n_top_words]

    # store the words most relevant to the topic
    words[topic] = [vocab[i] for i in indices]