import nltk
nltk.download()#下載nltk語料庫如果沒有安裝nltk,請在cmd下用批 pip install nltk進行安裝


from nltk.book import  *


###搜尋文字
#搜尋單詞
text1.concordance("monstrous")
text2.concordance("affection")
text3.concordance("lived")
text5.concordance("lol")


#搜尋相似詞
text1.similar("monstrous")


text2.similar("monstrous")


#搜尋共同上下文
text2.common_contexts(["monstrous", "very"])


#詞彙分佈圖
text4.dispersion_plot(["citizens", "democracy", "freedom", "duties", "America"])




###計數詞彙
len(text3)


sorted(set(text3))


len(set(text3))


#重複詞密度
from __future__ import division
len(text3) / len(set(text3))


#關鍵詞密度
text3.count("smote")


100 * text4.count('a') / len(text4)


def lexical_diversity(text): 
    return len(text) / len(set(text)) 
    
def percentage(count, total): 
    return 100 * count / total
    
lexical_diversity(text3)


lexical_diversity(text5)


percentage(4, 5)


percentage(text4.count('a'), len(text4))




###詞連結串列
sent1 = ['Call', 'me', 'Ishmael', '.']
sent1


len(sent1)


lexical_diversity(sent1)


print(sent2)
print(sent3)


#連線
sent4+sent1
#追加
sent1.append("some")
print(sent1)


#索引
text4[173]


text4.index('awaken')


#切片
print(text5[16715:16735])
print(text6[1600:1625])


#索引從0開始，要注意
sent = ['word1', 'word2', 'word3', 'word4', 'word5','word6', 'word7', 'word8', 'word9', 'word10']
print(sent[0])
print(sent[9])


print(sent[10])


print(sent[5:8])
print(sent[5])
print(sent[6])
print(sent[7])


print(sent[:3])
print(text2[141525:])


sent[0] = 'First'
sent[9] = 'Last'
sent[1:9] = ['Second', 'Third']
print(sent)
sent[9]


###簡單統計
#頻率分佈
fdist1 = FreqDist(text1)
fdist1


vocabulary1 = list(fdist1.keys())
vocabulary1[:50]


fdist1['whale']


fdist1.plot(50, cumulative=True)


fdist1.hapaxes()#尋找只出現過一次的詞


#細粒度的選擇詞
V = set(text4)
long_words = [w for w in V if len(w) > 15]
sorted(long_words)


V = set(text5)
long_words = [w for w in V if len(w) > 15]
sorted(long_words)


fdist5 = FreqDist(text5)
sorted([w for w in set(text5) if len(w) > 7 and fdist5[w] > 7])


#詞語搭配
from nltk.util import bigrams
list(bigrams(['more', 'is', 'said', 'than', 'done']))


text4.collocations()


text8.collocations()


###其他統計結果
[len(w) for w in text1]


fdist = FreqDist([len(w) for w in text1])
fdist
fdist.keys()


fdist.items()


fdist.max()


fdist[3]


fdist.freq(3)