import?nltk
import?math
import?string
from?nltk.corpus?import?stopwords
from?collections?import?Counter
from?nltk.stem.porter?import?*
from?sklearn.feature_extraction.text?import?TfidfVectorizer

text1?=?“Python?is?a?2000?made-for-TV?horror?movie?directed?by?Richard?\
Clabaugh.?The?film?features?several?cult?favorite?actors?including?William?\
Zabka?of?The?Karate?Kid?fame?Wil?Wheaton?Casper?Van?Dien?Jenny?McCarthy?\
Keith?Coogan?Robert?Englund?（best?known?for?his?role?as?Freddy?Krueger?in?the?\
A?Nightmare?on?Elm?Street?series?of?films）?Dana?Barron?David?Bowe?and?Sean?\
Whalen.?The?film?concerns?a?genetically?engineered?snake?a?python?that?\
escapes?and?unleashes?itself?on?a?small?town.?It?includes?the?classic?final\
girl?scenario?evident?in?films?like?Friday?the?13th.?It?was?filmed?in?Los?Angeles?\
?California?and?Malibu?California.?Python?was?followed?by?two?sequels:?Python?\
?II?（2002）?and?Boa?vs.?Python?（2004）?both?also?made-for-TV?films.“

text2?=?“Python?from?the?Greek?word?（π?θων/π?θωνα?）?is?a?genus?of?\
nonvenomous?pythons[2]?found?in?Africa?and?Asia.?Currently?7?species?are?\
recognised.[2]?A?member?of?this?genus?P.?reticulatus?is?among?the?longest?\
snakes?known.“

text3?=?“The?Colt?Python?is?a?.357?Magnum?caliber?revolver?formerly?\
manufactured?by?Colt‘s?Manufacturing?Company?of?Hartford?Connecticut.?\
It?is?sometimes?referred?to?as?a?\“Combat?Magnum\“.[1]?It?was?first?introduced?\
in?1955?the?same?year?as?Smith?&?Wesson‘s?M29?.44?Magnum.?The?now?discontinued?\
Colt?Python?targeted?the?premium?revolver?market?segment.?Some?firearm?\
collectors?and?writers?such?as?Jeff?Cooper?Ian?V.?Hogg?Chuck?Hawks?Leroy?\
Thompson?Renee?Smeets?and?Martin?Dougherty?have?described?the?Python?as?the?\
finest?production?revolver?ever?made.“

def?get_tokens（text）:
????lowers?=?text.lower（）
????#remove?the?punctuation?using?the?character?deletion?step?of?translate
????remove_punctuation_map?=?dict（（ord（char）?None）?for?char?in?string.punctuation）
????no_punctuation?=?lowers.translate（remove_punctuation_map）
????tokens?=?nltk.word_tokenize（no_punctuation）
????return?tokens

tokens?=?get_tokens（text1）
count?=?Counter（tokens）
print?（count.most_common（10））

def?stem_tokens（tokens?stemmer）:
????stemmed?=?[]
????for?item?in?tokens:
????????stemmed.append（stemmer.stem（item））
????return?stemmed

tokens?=?get_tokens（text1）
filtered?=?[w?for?w?in?tokens?if?not?w?in?stopwords.words（‘english‘）]
stemmer?=?PorterStemmer（）
stemmed?=?stem_tokens（filtered?stemmer）
count1?=?Counter（stemmed）
#?print?（count）

tokens?=?get_tokens（text2）
filtered?=?[w?for?w?in?tokens?if?not?w?in?stopwords.words（‘english‘）]
stemmer?=?PorterStemmer（）
stemmed?=?stem_tokens（filtered?stemmer）
count2?=?Counter（stemmed）
#?print?（count）

tokens?=?get_tokens（text3）
filtered?=?[w?for?w?in?tokens?if?not?w?in?stopwords.words（‘english‘）]
stemmer?=?PorterStemmer（）
stemmed?=?stem_tokens（filtered?stemmer）
coun

?屬性????????????大小?????日期????時間???名稱
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?????文件???????3796??2018-06-03?12:01??tf-idf.py

-----------?---------??----------?-----??----

?????????????????3796????????????????????1