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NLP_feature_extractor.py
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NLP_feature_extractor.py
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import nltk
from nltk import FreqDist,ngrams
from nltk.corpus import stopwords
import string
from os import listdir
from os.path import isfile, join
def wordfreq(file):
f = open(file,'rU')
raw = f.read()
raw = raw.replace('\n',' ')
#raw = raw.decode('utf8')
#tokenization
tokens = nltk.word_tokenize(raw)
#stopwords = stopwords.words('english') #use the NLTK stopwords
#lower everything
words = [w.lower() for w in tokens]
#words_nostop = [w.lower() for w in tokens]
#remove numbers
words = [w for w in words if w.isalpha()]
#words_nostop = [w for w in words_nostop if w.isalpha()]
#encode
words = [w.encode('utf8') for w in words]
#words_nostop = [w.encode('utf8') for w in words if w not in stopwords]
#remove punctuations
words = [w.translate(None, string.punctuation) for w in words]
#words_nostop = [w.translate(None, string.punctuation) for w in words_nostop]
freq = FreqDist(words)
#freq_nostop = FreqDist(words_nostop)
sorted_freq = sorted(freq.items(),key = lambda k:k[1], reverse = True)
#sorted_freq_nostop = sorted(freq_nostop.items(),key = lambda k:k[1], reverse = True)
return sorted_freq
# def sentiment(file):
# raw = f.read()
# raw = raw.replace('\n',' ')
# #raw = raw.decode('utf8')
# #tokenization
# tokens = nltk.word_tokenize(raw)
# for word in tokens:
def wordfreq(file):
f = open(file,'rU')
raw = f.read()
raw = raw.replace('\n',' ')
#raw = raw.decode('utf8')
#tokenization
tokens = nltk.word_tokenize(raw)
#stopwords = stopwords.words('english') #use the NLTK stopwords
#lower everything
words = [w.lower() for w in tokens]
#words_nostop = [w.lower() for w in tokens]
#remove numbers
words = [w for w in words if w.isalpha()]
#words_nostop = [w for w in words_nostop if w.isalpha()]
#encode
words = [w.encode('utf8') for w in words]
#words_nostop = [w.encode('utf8') for w in words if w not in stopwords]
#remove punctuations
words = [w.translate(None, string.punctuation) for w in words]
#words_nostop = [w.translate(None, string.punctuation) for w in words_nostop]
freq = FreqDist(words)
#freq_nostop = FreqDist(words_nostop)
sorted_freq = sorted(freq.items(),key = lambda k:k[1], reverse = True)
#sorted_freq_nostop = sorted(freq_nostop.items(),key = lambda k:k[1], reverse = True)
return sorted_freq
def postag(file,tag):
f = open(file,'rU')
raw = f.read()
print type(raw)
raw = raw.replace('\n',' ')
raw = raw.decode("utf-8", 'ignore')
#tokenization
tokens = nltk.word_tokenize(raw)
POS_tags = nltk.pos_tag(tokens)
POS_tags_list = [(word,tag) for (word,tag) in POS_tags if tag.startswith(tag)]
#freq = FreqDist(words)
#tag_freq = FreqDist(POS_tags_list)
#sorted_freq = sorted(freq.items(),key = lambda k:k[1], reverse = True)
#sorted_tag_freq = sorted(tag_freq.items(),key = lambda k:k[1], reverse = True)
return POS_tags_list
def ngram_list(file,n):
f = open(file,'rU')
raw = f.read()
raw = raw.replace('\n',' ')
#raw = raw.decode('utf8')
ngramz = ngrams(raw.split(),n)
return ngramz
mypath = '/Users/francis/Documents/FORDHAM/2nd Term/Text Analytics/' #path where files are located
onlyfiles = [f for f in listdir(mypath) if isfile(join(mypath, f))]
allwords_nostop = []
allpos_tags = []
allbigrams = []
alltrigrams = []
allfivegrams = []
tag_list = ['N','J','RB','V']
for i in onlyfiles:
if i.endswith('.txt'):
# get code
j = i.replace('.txt','')
# string filename
file = mypath + str(i)
print i
# allwords_nostop.append(wordfreq_nostop(file))
# print i + ' ALLWORDS_NOSTOP - OK'
for tag in tag_list:
allpos_tags.append(postag(file,tag))
print i + ' POSTAGGING - OK'
# allbigrams.append(ngram_list(file,2))
# print i + ' BIGRAM - OK'
# alltrigrams.append(ngram_list(file,3))
# print i + ' TRIGRAM - OK'
# allfivegrams.append(ngram_list(file,5))
# print i + ' TRIGRAM - OK'
else:
pass
AWNS = []
APT = []
BG = []
TG = []
FG = []
# for i in allwords_nostop:
# AWNS += i
# nostop_freq = FreqDist(AWNS)
# sorted_nostop_freq = sorted(nostop_freq.items(),key = lambda k:k[1], reverse = True)
for i in allpos_tags:
APT += i
tag_freq = FreqDist(APT)
sorted_tag_freq = sorted(tag_freq.items(),key = lambda k:k[1], reverse = True)
# for i in allbigrams:
# BG += i
# bg_freq = FreqDist(BG)
# sorted_bg_freq = sorted(bg_freq.items(),key = lambda k:k[1], reverse = True)
# for i in alltrigrams:
# TG += i
# tg_freq = FreqDist(TG)
#sorted_tg_freq = sorted(tg_freq.items(),key = lambda k:k[1], reverse = True)
# for i in allfivegrams:
# FG += i
# fg_freq = FreqDist(FG)
# sorted_fg_freq = sorted(fg_freq.items(),key = lambda k:k[1], reverse = True)
# a = 0
# for i in sorted_nostop_freq:
# print 'nonstop' +str(a)
# a+=1
# with open('sorted_nostop_freq.txt', 'a') as csvfile:
# csvfile.write(str(i) + '\n')
a = 0
for i in sorted_tag_freq:
print 'tag' +str(a)
a+=1
with open('sorted_tag_extended_freq.txt', 'a') as csvfile:
csvfile.write(str(i) + '\n')
# a = 0
# for i in sorted_bg_freq:
# print 'bg' +str(a)
# a+=1
# with open('sorted_bg_freq.txt', 'a') as csvfile:
# csvfile.write(str(i) + '\n')
# a = 0
# for i in sorted_tg_freq:
# print 'tg' +str(a)
# a+=1
# with open('sorted_tg_freq.txt', 'a') as csvfile:
# csvfile.write(str(i) + '\n')
# a = 0
# for i in sorted_fg_freq:
# print 'fg' +str(a)
# a+=1
# with open('sorted_fg_freq.txt', 'a') as csvfile:
# csvfile.write(str(i) + '\n')