python对excel里面的短文本做lda主题分析，根据一致性计算最佳主题数并运用，最终输出一个excel包含文本对应的全部主题和概率的代码

以下是一个用Python对Excel中的短文本进行LDA主题分析，并根据一致性计算最佳主题数并运用，最终输出包含文本对应的全部主题和概率的代码：

import pandas as pd
import numpy as np
import re
import nltk
from nltk.corpus import stopwords
from gensim.models import LdaModel
from gensim.corpora import Dictionary
from gensim.models.coherencemodel import CoherenceModel
from sklearn.feature_extraction.text import CountVectorizer

# 导入Excel数据
df = pd.read_excel('your_file_name.xlsx')
text_data = df['text'].tolist()

# 数据预处理：去除停用词、数字、标点符号和长度小于3的词语
nltk.download('stopwords')
en_stopwords = stopwords.words('english')
def preprocess(text):
    text = str(text)
    text = re.sub(r'\d+', '', text)
    text = text.lower()
    text = ' '.join([word for word in text.split() if word not in en_stopwords])
    text = re.sub(r'[^\w\s]','',text)
    text = ' '.join([word for word in text.split() if len(word) > 3])
    return text
cleaned_data = [preprocess(text) for text in text_data]

# 构建文本-词频矩阵
vectorizer = CountVectorizer()
doc_term_matrix = vectorizer.fit_transform(cleaned_data)

# 构建词典
id2word = Dictionary(vectorizer.get_feature_names())

# 构建LDA模型并计算一致性
coherence_scores = []
for num_topics in range(2, 11):
    lda_model = LdaModel(
        corpus=doc_term_matrix,
        id2word=id2word,
        num_topics=num_topics,
        random_state=100,
        chunksize=100,
        passes=10,
        alpha='auto',
        per_word_topics=True
    )
    coherence_model = CoherenceModel(
        model=lda_model,
        texts=cleaned_data,
        dictionary=id2word,
        coherence='c_v'
    )
    coherence_scores.append(coherence_model.get_coherence())

# 找到最佳主题数
best_num_topics = np.argmax(coherence_scores) + 2

# 构建LDA模型并输出结果到Excel
lda_model = LdaModel(
    corpus=doc_term_matrix,
    id2word=id2word,
    num_topics=best_num_topics,
    random_state=100,
    chunksize=100,
    passes=10,
    alpha='auto',
    per_word_topics=True
)
topic_list = []
for i, row_list in enumerate(lda_model[doc_term_matrix]):
    row = row_list[0] if lda_model.per_word_topics else row_list            
    row = sorted(row, key=lambda x: (x[1]), reverse=True)
    for j, (topic_num, prop_topic) in enumerate(row):
        if j == 0:
            wp = lda_model.show_topic(topic_num)
            topic_keywords = ", ".join([word for word, prop in wp])
            topic_list.append((i, topic_num, prop_topic, topic_keywords))
        else:
            break
df_topics = pd.DataFrame(topic_list, columns=['Document_Id', 'Topic_Num', 'Prop_Topic', 'Topic_Keywords'])
df_topics.to_excel('output_file_name.xlsx', index=False)

注意，需要将代码中的`your_file_name.xlsx`替换为包含输入数据的Excel文件名，并将`output_file_name.xlsx`替换为输出结果的Excel文件名。此外，还需要安装以下Python包：pandas、numpy、re、nltk、gensim和scikit-learn。