如何使用主题建模获取基于主题的所有关键字？

Question

我正在尝试使用lda的主题建模来分离主题。

在这里，我能够提取每个主题的前10个关键字。我试图获取每个主题中的所有关键字，而不是仅获取前10个关键字。

有人可以建议我...

我的代码：

from gensim.models import ldamodel
import gensim.corpora;
from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer;
from sklearn.decomposition import LatentDirichletAllocation
import warnings
warnings.filterwarnings("ignore",category=DeprecationWarning)


def load_data(filename):
    reviews = list()
    labels = list()
    with open(filename, encoding='utf-8') as file:
        file.readline()
        for line in file:
            line = line.strip().split(' ',1)
            labels.append(line[0])
            reviews.append(line[1])

    return reviews
data = load_data('/Users/abc/dataset.txt')
#print("Data:" , data)

def display_topics(model, feature_names, no_top_words):
    for topic_idx, topic in enumerate(model.components_):
        print ("Topic %d:" % (topic_idx))
        print (" ".join([feature_names[i]
                        for i in topic.argsort()[:-no_top_words - 1:-1]]))


no_features = 1000

tf_vectorizer = CountVectorizer(max_df=0.95, min_df=2, max_features=no_features, stop_words='english')
tf = tf_vectorizer.fit_transform(data)
tf_feature_names = tf_vectorizer.get_feature_names()

no_topics = 5

lda = LatentDirichletAllocation(n_topics=no_topics, max_iter=5, learning_method='online', learning_offset=50.,random_state=0).fit(tf)
no_top_words = 10


display_topics(lda, tf_feature_names, no_top_words)