13垃圾邮件分类

1.读取

file_path = r'D:\DingDing\main\current\download\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])) # 对每封邮件做预处理
sms.close()

2.数据预处理

 

# 根据词性，生成还原参数 pos
def get_wordnet_pos(treebank_tag):
if treebank_tag.startswith("J"):
return nltk.corpus.wordnet.ADJ
elif treebank_tag.startswith("V"):
return nltk.corpus.wordnet.VERB
elif treebank_tag.startswith("N"):
return nltk.corpus.wordnet.NOUN
elif treebank_tag.startswith("R"):
return nltk.corpus.wordnet.ADV
else:
return nltk.corpus.wordnet.NOUN

# 预处理
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
tokens = [token.lower() for token in tokens if len(token) >= 3]

tag = nltk.pos_tag(tokens) # 词性标注
imtzr = WordNetLemmatizer()
tokens = [imtzr.lemmatize(token, pos=get_wordnet_pos(tag[i][1])) for i, token in enumerate(tokens)] # 词性还原
preprocessed_text = ''.join(tokens)
return preprocessed_text

3.数据划分—训练集和测试集数据划分

from sklearn.model_selection import train_test_split

x_train,x_test, y_train, y_test = train_test_split(data, target, test_size=0.2, random_state=0, stratify=y_train)

 

4.文本特征提取

sklearn.feature_extraction.text.CountVectorizer

https://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.CountVectorizer.html?highlight=sklearn%20feature_extraction%20text%20tfidfvectorizer

sklearn.feature_extraction.text.TfidfVectorizer

https://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html?highlight=sklearn%20feature_extraction%20text%20tfidfvectorizer#sklearn.feature_extraction.text.TfidfVectorizer

from sklearn.feature_extraction.text import TfidfVectorizer

tfidf2 = TfidfVectorizer()

 

4.模型选择

from sklearn.naive_bayes import GaussianNB

from sklearn.naive_bayes import MultinomialNB

说明为什么选择这个模型？

5.模型评价：混淆矩阵，分类报告

from sklearn.metrics import confusion_matrix

confusion_matrix = confusion_matrix(y_test, y_predict)

说明混淆矩阵的含义

from sklearn.metrics import classification_report

说明准确率、精确率、召回率、F值分别代表的意义

 

 

 

6.比较与总结

如果用CountVectorizer进行文本特征生成，与TfidfVectorizer相比，效果如何？

CountVectorizer：特征数值计算类，文本特征提取方法。
对于每一个训练文本，CountVectorizer会将文本中的词语转换为词频矩阵，它通过fit_transform函数计算各个词语在该训练文本出现的次数。

TfidfVectorizer：可以把原始文本转化为tf-idf的特征矩阵,从而为后续的文本相似度计算，还关注其他包含这个词的文本，挖掘更有意义的特征。

后者比较灵活。