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ngram1.1.py
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# -*- coding: utf-8 -*-
"""
Created on Mon Sep 18 18:17:19 2017
@author: HCHO
"""
#collections 和 counter
#problem:关于停词和词频的选择
import re
import string
import operator
import os
class nGramAlgo(object):
def __init__(self,fiction):
self.fiction=fiction
#剔除常用字函数(停词)
def isCommon(self,ngram):
commonWords = ["a", "about", "above", "above", "across", "after", "afterwards",
"again", "against", "all", "almost", "alone", "along", "already",
"also","although","always","am","among", "amongst", "amoungst",
"amount", "an", "and", "another", "any","anyhow","anyone",
"anything","anyway", "anywhere", "are", "around", "as",
"at", "back","be","became", "because","become","becomes",
"becoming", "been", "before", "beforehand", "behind", "being",
"below", "beside", "besides", "between", "beyond", "bill",
"both", "bottom","but", "by", "call", "can", "cannot", "cant",
"co", "con", "could", "couldnt", "cry", "de", "describe",
"detail", "do", "done", "down", "due", "during", "each", "eg",
"eight", "either", "eleven","else", "elsewhere", "empty", "enough",
"etc", "even", "ever", "every", "everyone", "everything",
"everywhere", "except", "few", "fifteen", "fify", "fill",
"find", "fire", "first", "five", "for", "former", "formerly",
"forty", "found", "four", "from", "front", "full", "further",
"get", "give", "go", "had", "has", "hasnt", "have", "he",
"hence", "her", "here", "hereafter", "hereby", "herein",
"hereupon", "hers", "herself", "him", "himself", "his", "how",
"however", "hundred", "ie", "if", "in", "inc", "indeed",
"interest", "into", "is", "it", "its", "itself", "keep",
"last", "latter", "latterly", "least", "less", "ltd", "made",
"many", "may", "me", "meanwhile", "might", "mill", "mine",
"more", "moreover", "most", "mostly", "move", "much", "must",
"my", "myself", "name", "namely", "neither", "never", "nevertheless",
"next", "nine", "no", "nobody", "none", "noone", "nor", "not",
"nothing", "now", "nowhere", "of", "off", "often", "on", "once",
"one", "only", "onto", "or", "other", "others", "otherwise", "our",
"ours", "ourselves", "out", "over", "own","part", "per", "perhaps",
"please", "put", "rather", "re", "same", "see", "seem", "seemed",
"seeming", "seems", "serious", "several", "she", "should", "show",
"side", "since", "sincere", "six", "sixty", "so", "some", "somehow",
"someone", "something", "sometime", "sometimes", "somewhere",
"still", "such", "system", "take", "ten", "than", "that", "the",
"their", "them", "themselves", "then", "thence", "there",
"thereafter", "thereby", "therefore", "therein", "thereupon",
"these", "they", "thickv", "thin", "third", "this", "those",
"though", "three", "through", "throughout", "thru", "thus", "to",
"together", "too", "top", "toward", "towards", "twelve", "twenty",
"two", "un", "under", "until", "up", "upon", "us", "very", "via",
"was", "we", "well", "were", "what", "whatever", "when", "whence",
"whenever", "where", "whereafter", "whereas", "whereby", "wherein",
"whereupon", "wherever", "whether", "which", "while", "whither",
"who", "whoever", "whole", "whom", "whose", "why", "will", "with",
"within", "without", "would", "yet", "you", "your", "yours",
"yourself", "yourselves", "the",
"a","the","an",
"be","is","are","was","were","been","not","can","would","could","will",
"you","it","i","he","she","we",
"my","your","her","his","its", "our","their",
"me","him","her","they","them","us",
"this","that","these","those","here","there",
"and","but","or","so","if","because","than", "also", "before",
"of","in","to","with","on","at","from","as","by","about","for","into","up","out",
"what", "who","when", "which","where","how",
"i'm","i've","it's","don't",
"only","never",
"have","has","had", "do","did", "say","said","go","get","got","make","know","give","think","look","take",
"see","use", "find","want","like","come",
"one","all","some", "people","man","other","no","many","thing","little",
"time","day","month","year", "just","now", "then",
"two", "first", "new", "more","well","way","good"
]
if ngram in commonWords:
return True
else:
return False
def cleanText1(self):
fiction = re.sub('\n+', " ", self.fiction).lower() # 匹配换行用空格替换成空格,大写转换成小写
fiction = re.sub(' +', " ", fiction) # 把连续多个空格替换成一个空格
fiction = re.sub("‘+"," ",fiction)
return fiction
def cleanText2(self):
fiction = self.cleanText1()
fiction = fiction.split(' ') #以空格为分隔符，返回列表
cleanInput = []
for item in fiction:
item = item.strip(string.punctuation) # string.punctuation获取所有标点符号
if len(item) > 1 : #找出单词
cleanInput.append(item)
return cleanInput
def getNgrams(self, n):#n为划分词的数量
fiction = self.cleanText2()
#print (len(fiction))
output = {} # 构造字典
wordNum=0
for i in range(len(fiction)-n+1):
ngramTemp = " ".join(fiction[i:i+n])
#if self.isCommon(ngramTemp.split()[0]) or self.isCommon(ngramTemp.split()[1]):
'''
if self.isCommon(ngramTemp):
pass
else:
'''
wordNum+=1
if ngramTemp not in output: #词频统计
output[ngramTemp] = 1 #典型的字典操作
output[ngramTemp] += 1
#print (wordNum)
return output,wordNum
def select100words(self):
self.ngrams,self.words = self.getNgrams(1)
sortedNGrams = sorted(self.ngrams.items(), key = operator.itemgetter(1), reverse=True) #operator.itemgetter获取某个值 reverse=True 降序排列
count100=1 #计数，取词频最高的100个词
for num in sortedNGrams:
if count100>100 or num[1]==2:
self.ngrams.pop(num[0])
count100+=1
'''
if num[1]<max(5,words/600): #词频筛选条件
ngrams.pop(num[0])
#print(num)
#print (ngrams)
'''
def returnwords(self):
return self.words,self.ngrams
def printwords(self,filepath):
filepath.write(file.replace('.txt','')+' !'+str(self.words)+' !'+str(self.ngrams)+'\n') #按格式存储文件名、有用词数、词频
#获取核心词在的句子
'''
def getFirstSentenceContaining(ngram, content):
#print(ngram)
sentences = content.split(".")
for sentence in sentences:
if ngram in sentence:
return sentence
return ""
'''
def sentenceCal(fictionTxt): #句子长度向量统计
sentenceNumList=[0]*30
fictionTxt=fictionTxt.replace('...','.')
fictionList=re.split('[.?!]',fictionTxt)
for sen in fictionList:
sen.strip(string.punctuation)
try:
sentenceNumList[len(sen.split())]+=1
except:
print (len(sen.split()))
return sentenceNumList
if __name__ == '__main__':
#content= open('C:\\Users\\HCHO\\Desktop\\Julia Ward Howe.txt','r').read()
#对本地文件的读取，测试时候用，因为无需联网
#content = open("1.txt").read()
cfile=open("C:\\Users\\HCHO\\Desktop\\fictionKeyWords.csv","w")
sentenceLength={} #用于句子长度统计
path="C:\\Users\\HCHO\\Desktop\\short fictions\\"
for root , dirs, files in os.walk(path):
for file in files:
txt=open(path+file,'r')
content=txt.read()
sentenceLength[file.replace('.txt','')]=sentenceCal(content)
result=nGramAlgo(content)
result.select100words()
result.printwords(cfile)
txt.close()
cfile.close()
'''
for top3 in range(3):
print ("###"+getFirstSentenceContaining(sortedNGrams[top3][0],content.lower())+"###")
'''