Question

我有一组推文数据，其中包含与疫苗感知相关的关键字。这些包括像

这样的词

[jab, shot, measles, MMR, vaccine, autism,...]。

我希望能够将新推文分类为Pro-vaccine，anti-vaccine或者两者都没有。我知道Naive Bayes就是这样做的一种方式。

我宁愿使用SKlearns库来实现分类算法，因为那些算法比我能写的更强大。

如何实施朴素贝叶斯？从Sklearn的网站来看，似乎我的选择是多项式和高斯式的，但我不确定使用哪种。

Answer 1

以下是对5种疾病进行分类的分类器的简单实现。

它有两个文件：

训练档案（train.txt）
测试文件（test.txt）

基本上，根据您的问题，您应该在Train文件中包含您的推文。您要在Test文件中分类的推文。

[注意：您还可以使用CSV或JSON表示来加载数据集，为了简单起见，我使用了文本文件。]

列车档案内容：[train.txt]

A highly contagious virus spread by coughing, sneezing or direct contact with skin lesions.
A contagious liver disease often caused by consuming contaminated food or water. It is the most common vaccine-preventable travel disease.
A serious liver disease spread through contact with blood or body fluids. The hepatitis B virus can cause liver cancer and possible death.
A group of over 100 viruses that spreads through sexual contact. HPV strains may cause genital warts and lead to cervical cancer.
A potentially fatal bacterial infection that strikes an average of 1,500 Americans annually.

测试文件的内容：[test.txt]

died due to liver cancer.

分类代码：[classifier.py]

import codecs
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.naive_bayes import MultinomialNB
trainfile = 'train.txt'
testfile = 'test.txt'
word_vectorizer = CountVectorizer(analyzer='word')
trainset = word_vectorizer.fit_transform(codecs.open(trainfile,'r','utf8'))
tags = ['CHICKEN POX','HEPATITIS A','HEPATITIS B','Human papillomavirus','MENINGITIS']
mnb = MultinomialNB()
mnb.fit(trainset, tags)
codecs.open(testfile,'r','utf8')
testset = word_vectorizer.transform(codecs.open(testfile,'r','utf8'))
results = mnb.predict(testset)
print results

为Twitter数据实现朴素贝叶斯

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