在机器学习领域中，朴素贝叶斯是一种基于贝叶斯定理的简单概率分类器, 朴素贝叶斯在处理文本数据时可以得到较好的分类结果，被广泛应用于文本分类/垃圾邮件过滤/自然语言处理等场景。

使用Python进行文本分类

判断某句话是否为正常言论

导入数据

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def loadDataSet():
    postingList = 
    [['my', 'dog', 'has', 'flea', 'problems', 'help', 'please'],
     ['maybe', 'not', 'take', 'him', 'to', 'dog', 'park', 'stupid'],
     ['my', 'dalmation', 'is', 'so', 'cute', 'I', 'love', 'him'],
     ['stop', 'posting', 'stupid', 'worthless', 'garbage'],
     ['mr', 'licks', 'ate', 'my', 'steak','how', 'to', 'stop', 'him'],
     ['quit', 'buying', 'worthless', 'dog', 'food', 'stupid']]
    classVec = [0, 1, 0, 1, 0, 1]  # 1 侮辱性文字, 0 正常言论
    return postingList, classVec

创建字典

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def createVocabList(dataSet):
    vocabSet = set([])  # create empty set
    for document in dataSet:
        vocabSet = vocabSet | set(document)  # union of the two sets
    return list(vocabSet)

将某句话转换为向量

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def setOfWords2Vec(vocabList, inputSet):
    returnVec = [0]*len(vocabList)
    for word in inputSet:
        if word in vocabList:
            returnVec[vocabList.index(word)] = 1	
            #词集模型 某个单词只能出现一次
        else:
            print("the word: %s is not in my Vocabulary!" % word)
    return returnVec

从词向量计算概率

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from numpy import *
import math
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def trainNB0(trainMatrix, trainCategory):
    numTrainDocs = len(trainMatrix)
    numWords = len(trainMatrix[0])
    pAbusive = sum(trainCategory)/float(numTrainDocs)
    p0Num = ones(numWords)
    p1Num = ones(numWords)  # change to ones()
    p0Denom = 2.0
    p1Denom = 2.0  # change to 2.0
    for i in range(numTrainDocs):
        if trainCategory[i] == 1:
            p1Num += trainMatrix[i]
            p1Denom += sum(trainMatrix[i])
        else:
            p0Num += trainMatrix[i]
            p0Denom += sum(trainMatrix[i])
    p1Vect = log(p1Num/p1Denom)  # change to log()
    p0Vect = log(p0Num/p0Denom)  # change to log()
    return p0Vect, p1Vect, pAbusive

参考文档