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))

结果如下：

对于整体代码和语料的下载，可以去下载