1.读取
2.数据预处理
词性还原
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
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 feature_extraction text tfidfvectorizer
sklearn.feature_extraction.text.TfidfVectorizer
https://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html?highlight=sklearn feature_extraction text tfidfvectorizer#sklearn.feature_extraction.text.TfidfVectorizer
from sklearn.feature_extraction.text import TfidfVectorizer
tfidf2 = TfidfVectorizer()
观察邮件与向量的关系
向量还原为邮件
from sklearn.feature_extraction.text import TfidfVectorizer
vectorizer = TfidfVectorizer()
X_train = vectorizer.fit_transform(x_train)
X_test = vectorizer.transform(x_test)
print(X_train.toarray().shape)
print(X_test.toarray().shape)
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