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- import pandas as pd
- from gensim.models import LdaModel
- from gensim.corpora import Dictionary
- from wordcloud import WordCloud
- import matplotlib
- import matplotlib.pyplot as plt
- matplotlib.rcParams['font.sans-serif'] = ['SimHei']
- matplotlib.rcParams['axes.unicode_minus'] = False
- # 读取CSV文件
-
-
- import jieba
- from gensim import corpora, models
- import re
-
- # 读取文本数据
- csv_file_path = '合并.csv'
- df = pd.read_csv(csv_file_path)
-
- # 将文本数据转换为列表
- text_data = df['登革热是蚊子传播的,这个和新冠没关系吧?'].tolist()
- print(text_data)
- # 分词处理
- texts = [[word for word in jieba.cut(document)] for document in text_data]
- textss=[]
- for line in texts:
- temp=[]
- for w in line:
- if len(str(w))>2:
- temp.append(w)
- if len(temp)>2:
- textss.append(temp)
- # print(texts)
- # 创建词袋模型
- dictionary = corpora.Dictionary(textss)
-
- # 转换文档为词袋表示
- corpus = [dictionary.doc2bow(text) for text in texts]
-
- # 训练LDA模型
- lda_model = LdaModel(corpus, id2word=dictionary, num_topics=10)
-
- # 打印主题词
- topics = lda_model.print_topics(num_words=5)
- for topic in topics:
- print(topic)
-
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