12.朴素贝叶斯-垃圾邮件分类

nltk库的安装与使用

import nltk

print(nltk.__doc__)

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')

*如果提示需要下载punkt

nltk.download(‘punkt’)

或 下载punkt.zip

https://pan.baidu.com/s/1OwLB0O8fBWkdLx8VJ-9uNQ  密码：mema

复制到对应的失败的目录C:\Users\Administrator\AppData\Roaming\nltk_data\tokenizers并解压。

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

import csv
import nltk

# 预处理
from nltk import WordNetLemmatizer
from nltk.corpus import stopwords


# 写一个方法进行词性还原
def get_wordnet_pos(treebank_tag):
    if treebank_tag.startswith('J'):  # 形容词
        return nltk.corpus.wordnet.ADJ
    elif treebank_tag.startswith('V'):  # 动词
        return nltk.corpus.wordnet.VERB
    elif treebank_tag.startswith('N'):  # 名词
        return nltk.corpus.wordnet.NOUN
    elif treebank_tag.startswith('R'):  # 副词
        return nltk.corpus.wordnet.ADV
    else:
        return


def preprocessing(text):
    tokens = [word for sent in nltk.sent_tokenize(text) for word in nltk.word_tokenize(sent)]  # 分词
    stops = stopwords.words("english")  # 停用词
    tokens = [token for token in tokens if token not in stops]  # 去掉停用词
    lemmatizer = WordNetLemmatizer()
    tag = nltk.pos_tag(tokens)  # 词性标注
    newtokens = []
    for i, token in enumerate(tokens):
     if token:
      pos = get_wordnet_pos(tag[i][1])
      if pos:
         word = lemmatizer.lemmatize(token, pos)
         newtokens.append(word)
    return newtokens


file_path = r'C:\Users\86186\Desktop\大三下\机器学习\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]))  # 对每封邮件进行预处理
sms.close()
print("邮件标签：", sms_label)
print("预处理后的邮件内容：", sms_data)

结果如下：