python 文本情感分类_python 中文文本情感分类

      对于一个简单的文本情感分类来说，其实就是一个二分类，这篇博客主要讲述的是使用scikit-learn来做文本情感分类。分类主要分为两步：1）训练，主要根据训练集来学习分类模型的规则。2）分类，先用已知的测试集评估分类的准确率等，如果效果还可以，那么该模型对无标注的待测样本进行预测。

     首先先介绍下我样本集，样本是已经分好词的酒店评论，第一列为标签，第二列为评论，前半部分为积极评论，后半部分为消极评论，格式如下：

下面实现了SVM,NB,逻辑回归，决策树，逻辑森林，KNN 等几种分类方法，主要代码如下：

#coding:utf-8
 
from matplotlib import pyplot  
import scipy as sp  
import numpy as np  
from sklearn.cross_validation import train_test_split  
from sklearn.feature_extraction.text import  CountVectorizer  
from sklearn.feature_extraction.text import  TfidfVectorizer   
from sklearn.metrics import precision_recall_curve  
from sklearn.metrics import classification_report  
from numpy import * 
 
 
 
#========SVM========#
def SvmClass(x_train, y_train):
	from sklearn.svm import SVC 
	#调分类器  
	clf = SVC(kernel = 'linear',probability=True)#default with 'rbf' 
	clf.fit(x_train, y_train)#训练，对于监督模型来说是 fit(X, y)，对于非监督模型是 fit(X)
	return clf
 
#=====NB=========#
def NbClass(x_train, y_train):
	from sklearn.naive_bayes import MultinomialNB
	clf=MultinomialNB(alpha=0.01).fit(x_train, y_train) 
	return clf
 
#========Logistic Regression========#
def LogisticClass(x_train, y_train):
	from sklearn.linear_model import LogisticRegression
	clf = LogisticRegression(penalty='l2')
	clf.fit(x_train, y_train)
	return clf
	
#========KNN========#
def KnnClass(x_train,y_train):
	from sklearn.neighbors import KNeighborsClassifier
	clf=KNeighborsClassifier()
	clf.fit(x_train,y_train)
	return clf
 
 
#========Decision Tree ========#
def DccisionClass(x_train,y_train):
	from sklearn import tree
	clf=tree.DecisionTreeClassifier()
	clf.fit(x_train,y_train)
	return clf
	
 
#========Random Forest Classifier ========#
def random_forest_class(x_train,y_train):
	from sklearn.ensemble import RandomForestClassifier
	clf= RandomForestClassifier(n_estimators=8)#参数n_estimators设置弱分类器的数量
	clf.fit(x_train,y_train)
	return clf
 
#========准确率召回率 ========#
def Precision(clf):
	doc_class_predicted = clf.predict(x_test) 
	print(np.mean(doc_class_predicted == y_test))#预测结果和真实标签
	#准确率与召回率  
	precision, recall, thresholds = precision_recall_curve(y_test, clf.predict(x_test))  
	answer = clf.predict_proba(x_test)[:,1]  
	report = answer > 0.5  
	print(classification_report(y_test, report, target_names = ['neg', 'pos']))
	print("--------------------")
	from sklearn.metrics import accuracy_score
	print('准确率: %.2f' % accuracy_score(y_test, doc_class_predicted))
 
 
if __name__ == '__main__':
	data=[]
	labels=[]
	with open ("train2.txt","r")as file:
		for line in file:
			line=line[0:1]
			labels.append(line)
	with open("train2.txt","r")as file:
		for line in file:
			line=line[1:]
			data.append(line)
	x=np.array(data)
	labels=np.array(labels)
	labels=[int (i)for i in labels]
	movie_target=labels
	#转换成空间向量
	count_vec = TfidfVectorizer(binary = False)
	#加载数据集，切分数据集80%训练，20%测试  
	x_train, x_test, y_train, y_test= train_test_split(x, movie_target, test_size = 0.2)  
	x_train = count_vec.fit_transform(x_train)  
	x_test  = count_vec.transform(x_test)
	
	print('**************支持向量机************  ')
	Precision(SvmClass(x_train, y_train))
	print('**************朴素贝叶斯************  ')
	Precision(NbClass(x_train, y_train))
	print('**************最近邻ＫＮＮ************  ')
	Precision(KnnClass(x_train,y_train))
	print('**************逻辑回归************  ')
	Precision(LogisticClass(x_train, y_train))
	print('**************决策树************  ')
	Precision(DccisionClass(x_train,y_train))
	print('**************逻辑森林************  ')
	Precision(random_forest_class(x_train,y_train))

对于整体代码和语料的下载，可以去下载