把上面的文本,换成text的文档,文档里边是分好词的中文,且每一行中文代表一个文档。再重新给出python实现LDA的具体实例,
时间: 2024-10-09 10:07:08 浏览: 14
如果你的文本文件是以分词后的形式存在,每行是一篇文档,那么首先需要读取这个文本文件并将其转换为gensim能处理的格式。假设文件名是`documents.txt`,下面是如何处理的示例:
```python
import jieba
from gensim.corpora import Dictionary
from gensim.models import LdaModel
# 读取分词后的文本文件
with open('documents.txt', 'r', encoding='utf-8') as f:
lines = f.readlines()
# 分词并将文档转换为list of lists
texts = [[word for word in line.strip().split()] for line in lines]
# 创建词典
dictionary = Dictionary(texts)
# 文档-词频矩阵
corpus = [dictionary.doc2bow(text) for text in texts]
# 设置主题数
num_topics = 5
# 训练LDA模型
lda_model = LdaModel(corpus, id2word=dictionary, num_topics=num_topics, passes=10)
# 输出主题及关键词
for topic_id, top_words in lda_model.show_topics(num_topics, formatted=True):
print(f"Topic {topic_id}: {top_words}")
# 对新文档进行分析
new_document = ['人工智能', '深度学习']
new_bow = dictionary.doc2bow(new_document)
new_topic_distribution = lda_model[new_bow]
print("New document's topic distribution:", new_topic_distribution)