我正在对一份文本数据进行LDA分析,使用了这个示例:这里。
我的问题是:
我如何知道哪些文档对应哪些主题?
换句话说,例如主题1的文档在讨论什么?
以下是我的步骤:
在tf数据上运行LDA:
使用上面的函数打印结果: ```html
n_features = 1000
n_topics = 8
n_top_words = 20
我逐行读取我的文本文件:
with open('dataset.txt', 'r') as data_file:
input_lines = [line.strip() for line in data_file.readlines()]
mydata = [line for line in input_lines]
一个打印主题的函数:
def print_top_words(model, feature_names, n_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()[:-n_top_words - 1:-1]]))
print()
对数据进行向量化处理:
tf_vectorizer = CountVectorizer(max_df=0.95, min_df=2, token_pattern='\\b\\w{2,}\\w+\\b',
max_features=n_features,
stop_words='english')
tf = tf_vectorizer.fit_transform(mydata)
初始化LDA:
lda = LatentDirichletAllocation(n_topics=3, max_iter=5,
learning_method='online',
learning_offset=50.,
random_state=0)
在tf数据上运行LDA:
lda.fit(tf)
使用上面的函数打印结果: ```html
使用上面的函数打印结果:
```print("\nTopics in LDA model:")
tf_feature_names = tf_vectorizer.get_feature_names()
print_top_words(lda, tf_feature_names, n_top_words)
打印输出的结果是:
Topics in LDA model:
Topic #0:
solar road body lamp power battery energy beacon
Topic #1:
skin cosmetic hair extract dermatological aging production active
Topic #2:
cosmetic oil water agent block emulsion ingredients mixture