对于文本分类，最简单的定义就是基于文本内容来对其进行分类。通常情况算法是根据数字/变量特征来写的。
先从https://archive.ics.uci.edu/ml/datasets/SMS+SPam+Collection中下载数据集
接下来先要进行数据清理，标识化处理，以及词干提取来对SMS数据进行清理，使其内容更加简单一点：

import nltk
from nltk.corpus import stopwords
from nltk.stem import WordNetLemmatizer
import csv

def preprocessing(text):
   # text=text.decode("utf8")
    tokens=[word for sent in nltk.sent_tokenize(text) for word in nltk.word_tokenize(sent)]
    stop=stopwords.words('english')
    tokens=[token for token in tokens if token not in stop]
    tokens=[word for word in tokens if len(word)>=3]
    tokens=[word.lower() for word in tokens]
    lmtzr=WordNetLemmatizer()
    tokens=[lmtzr.lemmatize(word)for word in tokens]
    preprocessed_text=' '.join(tokens)
    return  preprocessed_text

接下来分别创建两个列表分别存储清理后SMS的内容和标签

smsdata=open('SMSSpamCollection',encoding='UTF-8')
smsdata_data=[]
sms_labels=[]
csv_reader=csv.reader(smsdata,delimiter='\t')
for line in csv_reader:
    sms_labels.append(line[0])
    smsdata_data.append(preprocessing(line[1]))
smsdata.close()

接下来介绍取样操作，其思想就是机器学习中的将训练集分为两部分，一部分训练集，一部分验证集，一种防止过拟合的方法。
下面介绍了一个对语料库进行7，3分的操作：

import sklearn
import numpy as np
trainset_size=int(round(len(smsdata_data)*0.70))
print('The training set size for this classifier is '+str(trainset_size)+'\n')

x_train=np.array([''.join(el) for el in smsdata_data[0:trainset_size]])
y_train=np.array([el for el in sms_labels[0:trainset_size]])
x_test=np.array([''.join(el)for el in smsdata_data[trainset_size+1:len(smsdata_data)]])
y_test=np.array([el for el in sms_labels[trainset_size+1:len(smsdata_data)]])
print(x_train)
print(y_train)

结果：

[ 'jurong point crazy.. available bugis great world buffet ... cine got amore wat ...'
 'lar ... joking wif oni ...'
 'free entry wkly comp win cup final tkts 21st may 2005 text 87121 receive entry question std txt rate apply 08452810075over18'
 ...,
 'tell call 09066358152 claim £5000 prize enter mobile personal detail prompt careful'
 "thank you 've wonderful" 'otherwise part time job na-tuition..']
['ham' 'ham' 'spam' ..., 'spam' 'ham' 'ham']

下面介绍了：词汇文档矩阵（term-document matrix），就是将文本转换为矩阵，文本文档也可以用bow（bag of word）表示
如果要用python生成一个类似词汇矩阵，要用到sklearn中的向量化器：

from sklearn.feature_extraction.text import CountVectorizer
sms_exp=[ ]
for line in csv_reader:
    sms_exp.append(preprocessing(line[1]))
vectorizer=CountVectorizer(min_df=1)
X_exp=vectorizer.fit_transform(sms_exp)
print("||".join(vectorizer.get_feature_names()))
print(X_exp.toarray())

结果：

00||000||000pes||008704050406||0089||0121||01223585236||01223585334||0125698789||02||0207||02072069400||02073162414||02085076972||021||03||04||0430||05||050703||0578||06||07||07008009200||07046744435||07090201529||07090298926||07099833605||07123456789||0721072||07732584351||07734396839||07742676969||07753741225||0776xxxxxxx||07781482378||07786200117||077xxx||078||07801543489||07808||07808247860||07808726822||07815296484||07821230901||078498||07880867867||0789xxxxxxx||07946746291||0796xxxxxx||07973788240||07xxxxxxxxx||08||0800||08000407165||08000776320||08000839402||08000930705||08000938767||08001950382||08002888812||08002986030||08002986906||08002988890||08006344447||0808||08081263000||08081560665||0825||083||0844||08448350055||08448714184||0845||08450542832||08452810071||08452810073||08452810075over18||0870||08700435505150p||08700469649||08700621170150p||08701213186||08701237397||08701417012||08701417012150p||0870141701216||087016248||08701752560||087018728737||0870241182716||08702490080||08702840625||08704050406||08704439680||08704439680ts||08706091795||0870737910216yrs||08707500020||08707509020||0870753331018||08707808226||08708034412||08708800282||08709222922||08709501522||0871||087104711148||08712101358||08712103738||0871212025016||08712300220||087123002209am||08712317606||08712400200||08712400602450p||08712400603||08712402050||08712402578||08712402779||08712402902||08712402972||08712404000||08712405020||08712405022||08712460324||08712466669||0871277810710p||0871277810810||0871277810910p||08714342399||087147123779am||08714712379||08714712388||08714712394||08714712412||08714714011||08715203028||08715203649||08715203652||08715203656||08715203677||08715203685||08715203694||08715205273||08715500022||08715705022||08717111821||08717168528||08717205546||0871750||08717507382||08717509990||08717890890||08717895698||08717898035||08718711108||08718720201||08718723815||08718725756||08718726270||087187262701||08718726970||08718726971||08718726978||087187272008||08718727868||08718727870||08718727870150ppm||08718730555||08718730666||08718738001||08718738002||08718738034||08719180219||08719180248||08719181259||08719181503||08719181513||08719839835||08719899217||08719899229||08719899230||09||09041940223||09050000301||09050000332||09050000460||09050000555||09050000878||09050000928||09050001295||09050001808||09050002311||09050003091||09050005321||09050090044||09050280520||09053750005||09056242159||09057039994||09058091854||09058091870||09058094454||09058094455||09058094507||09058094565||09058094583||09058094594||09058094597||09058094599||09058095107||09058095201||09058097189||09058097218||09058098002||09058099801||09061104276||09061104283||09061209465||09061213237||09061221061||09061221066||09061701444||09061701461||09061701851||09061701939||09061702893||09061743386||09061743806||09061743810||09061743811||09061744553||09061749602||09061790121||09061790125||09061790126||09063440451||09063442151||09063458130||0906346330||09064011000||09064012103||09064012160||09064015307||09064017295||09064017305||09064018838||09064019014||09064019788||09065069120||09065069154||09065171142||09065174042||09065394514||09065394973||09065989180||09065989182||09066350750||09066358152||09066358361||09066361921||09066362206||09066362220||09066362231||09066364311||09066364349||09066364589||09066368327||09066368470||09066368753||09066380611||09066382422||09066612661||09066649731from||09066660100||09071512432||09071512433||09071517866||09077818151||09090204448||09090900040||09094100151||09094646631||09094646899||09095350301||09096102316||09099725823||09099726395||09099726429||09099726481||09099726553||09111030116||09111032124||09701213186||0quit||10||100||1000||1000call||100p||100percent||100txt||1013||1030||10am||10k||10p||10ppm||10th||11||1120||113||1131||114||1146||116||1172||118p||11mths||11pm||12||1205||120p||121||1225||123||125||1250||125gift||128||12hours||12hrs||12mths||13||130||1327||139||14||140||1405||140ppm||145||1450||146tf150p||14tcr||14thmarch||15||150||1500||150p||150p16||150pm||150ppermesssubscription||150ppm||150ppmpobox10183bhamb64xe||150ppmsg||150pw||151||153||15541||15pm||16||165||1680||169||177||18||180||1843||18p||18yrs||195||1956669||1apple||1b6a5ecef91ff9||1cup||1da||1er||1hr||1im||1lemon||1mega||1million||1pm||1st||1st4terms||1stchoice||1stone||1thing||1tulsi||1win150ppmx3||1winaweek||1winawk||1x150p||1yf||20||200||2000||2003||2004||2005||2006||2007||200p||2025050||20m12aq||20p||21||21870000||21st||22||220||220cm2||2309||23f||23g||24||24hrs||24m||24th||25||250||250k||255||25p||26||2667||26th||27||28||2814032||28days||28th||28thfeb||29||2bold||2channel||2day||2docd||2end||2exit||2ez||2find||2getha||2geva||2go||2gthr||2hook||2hrs||2kbsubject||2lands||2marrow||2moro||2morow||2morro||2morrow||2morrowxxxx||2mro||2mrw||2mwen||2nd||2nhite||2nights||2nite||2optout||2price||2px||2rcv||2stop||2stoptx||2stoptxt||2u2||2watershd||2waxsto||2wks||2wt||2wu||2years||2yr||2yrs||30||300||3000||300603||300603t||300p||3030||30apr||30ish||30pm||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用计算向量会遇到一些问题：即较长文档所获得的平均计数值会高于较短文档；
一个较好的解决方法是用文档中每一个单词出现的次数除以该文档中单词总数就可以了，这个特征值叫做tf（term frequencies）
tf之上还有另一个改进，对于语料库中文字中出现的词汇进行降维加权，可以减少语料库中某一小部分中出现的信息，这种方法叫做tf-idf（term frequency-inverse document frequency）

from sklearn.feature_extraction.text import TfidfVectorizer
vectorizer=TfidfVectorizer(min_df=2,ngram_range=(1,2),stop_words='english',strip_accents='unicode',norm='l2')
X_train=vectorizer.fit_transform(x_train)
X_test=vectorizer.fit_transform(x_test)
print(x_train)
print(x_test)

结果：

[ 'jurong point crazy.. available bugis great world buffet ... cine got amore wat ...'
 'lar ... joking wif oni ...'
 'free entry wkly comp win cup final tkts 21st may 2005 text 87121 receive entry question std txt rate apply 08452810075over18'
 ...,
 'tell call 09066358152 claim £5000 prize enter mobile personal detail prompt careful'
 "thank you 've wonderful" 'otherwise part time job na-tuition..']
['okie' 'that depends how would like treated' 'right brah see later' ...,
 'pity mood ... suggestion'
 'the guy bitching acted like interested buying something else next week gave free'
 'rofl it true name']

得到一个矩阵格式的文本X_train和X_test