12.朴素贝叶斯-垃圾邮件分类

朴素贝叶斯垃圾邮件分类

1. 读邮件数据集文件，提取邮件本身与标签。

2.邮件预处理

2.1传统方法

2.1 nltk库 分词
nltk.sent_tokenize(text) #对文本按照句子进行分割

nltk.word_tokenize(sent) #对句子进行分词

2.2 punkt 停用词
from nltk.corpus import stopwords

stops=stopwords.words('english')

2.3 NLTK 词性标注
nltk.pos_tag(tokens)

2.4 Lemmatisation（词性还原）
from nltk.stem import WordNetLemmatizer

lemmatizer = WordNetLemmatizer()

lemmatizer.lemmatize('leaves') #缺省名词

lemmatizer.lemmatize('best',pos='a')

lemmatizer.lemmatize('made',pos='v')

一般先要分词、词性标注，再按词性做词性还原。

2.5 编写预处理函数
def preprocessing(text):

sms_data.append(preprocessing(line[1])) #对每封邮件做预处理

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import csv
import nltk
from mistune import preprocessing
from nltk.corpus import stopwords
from nltk.stem import WordNetLemmatizer

def preprocessing(text):
# 分词
fenge = []
for sent in nltk.sent_tokenize(text):
for word in nltk.word_tokenize(sent):
fenge.append(word)
# 停用词
stops = stopwords.words("english")
tingyong = [i for i in fenge if i not in stops]
# 磁性标注
nltk.pos_tag(tingyong)
# 磁性还原
lemmatizer = WordNetLemmatizer()
huanyuan = []
for i in tingyong:
huanyuan.append(lemmatizer.lemmatize(i, pos='v'))
for i in tingyong:
huanyuan.append(lemmatizer.lemmatize(i, pos='a'))
for i in tingyong:
huanyuan.append(lemmatizer.lemmatize(i, pos='n'))

return huanyuan

file_path=r'C:\Users\we\Desktop\SMSSpamCollection'
sms=open(file_path,'r',encoding='utf-8')
sms_data=[]
sms_label=[]
csv_reader=csv.reader(sms,delimiter='\t')
for line in csv_reader:
sms_label.append(line[0])
sms_data.append(preprocessing(line[1]))
sms.close()

print("分词标注停用还原后的数据",sms_data[1:10])
print("邮件分类2",sms_label)
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3. 训练集与测试集

4. 词向量

5. 模型