#coding=utf8import numpy as npimport pandas as pdimport refrom gensim import corpora, models, similaritiesimport gensimfrom nltk.corpus import stopwordsdf = pd.read_csv("./input/HillaryEmails.csv")# 原邮件数据中有很多Nan的值，直接扔了。df = df[['Id', 'ExtractedBodyText']].dropna()def clean_email_text(text):    text = text.replace('\n'," ") #新行，我们是不需要的    text = re.sub(r"-", " ", text) #把 "-" 的两个单词，分开。（比如：july-edu ==> july edu）    text = re.sub(r"\d+/\d+/\d+", "", text) #日期，对主体模型没什么意义    text = re.sub(r"[0-2]?[0-9]:[0-6][0-9]", "", text) #时间，没意义    text = re.sub(r"[\w]+@[\.\w]+", "", text) #邮件地址，没意义    text = re.sub(r"/[a-zA-Z]*[:\//\]*[A-Za-z0-9\-_]+\.+[A-Za-z0-9\.\/%&=\?\-_]+/i", "", text) #网址，没意义    pure_text = ''    # 以防还有其他特殊字符（数字）等等，我们直接把他们loop一遍，过滤掉    for letter in text:        # 只留下字母和空格        if letter.isalpha() or letter==' ':            pure_text += letter    # 再把那些去除特殊字符后落单的单词，直接排除。    # 我们就只剩下有意义的单词了。    text = ' '.join(word for word in pure_text.split() if len(word)>1)    return textdocs = df['ExtractedBodyText']docs = docs.apply(lambda s: clean_email_text(s))doclist = docs.valuesstopwords = set(stopwords.words('english'))texts = [[word for word in doc.lower().split() if word not in stopwords] for doc in doclist]dictionary = corpora.Dictionary(texts)corpus = [dictionary.doc2bow(text) for text in texts]lda = gensim.models.ldamodel.LdaModel(corpus=corpus, id2word=dictionary, num_topics=20)print lda.print_topics(num_topics=20, num_words=5)