content: import csv
  import pandas as pd
  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 ngram_list(file,n):
      f = open(file,'rU')
      raw = f.read()
      raw = raw.replace('\n',' ')
      #raw = raw.decode('utf8')
      #raw = raw.decode("utf-8", 'ignore')
      ngramz = ngrams(raw.split(),n)
      return ngramz
  def IsNotNull(value):
  	    return value is not None and len(value) > 0
  
  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))]
  
  dict_p = []
  f = open('positive.txt', 'r')
  for line in f:
  	t = line.strip().lower()
  	if IsNotNull(t):
  		dict_p.append(t)
  f.close
  
  dict_n = []
  f = open('negative.txt', 'r')
  for line in f:
  	t = line.strip().lower()
  	if IsNotNull(t):
  		dict_n.append(t)
  f.close
  totallist = []
  rowlist = []
  qa = 0
  qb = 0
  counti = 0
  for i in onlyfiles:
      if i.endswith('.txt'):
          # get code
          j = i.replace('.txt','')
          # string filename
          file = mypath + str(i)
          print i
  
          f = open(file,'rU')
          raw = f.read()
          #print type(raw)
          raw = [w.translate(None, string.punctuation) for w in raw]
          raw = ''.join(raw)
      	raw = raw.replace('\n','')
      	raw = raw.replace(' ','')
      	#print raw
      	qa = 0
      	qb = 0
          for word in dict_p:
  			if word in raw:
  				qa += 1
  
      	for word in dict_n:
  			if word in raw:
  				qb += 1
  
      	qc = qa - qb
  
      	if qc > 0:
  			sentiment = 'POSITIVE'
      	elif qc == 0:
  			sentiment = 'NEUTRAL'
      	else:
  			sentiment = 'NEGATIVE'
  
      	rowlist.append(i)
      	rowlist.append(qa)
      	rowlist.append(qb)
      	rowlist.append(qc)
      	rowlist.append(sentiment)
  
      	print counti
      	counti += 1
      	totallist.append(rowlist)
  
      	rowlist = []
  
  
  
  
      else:
          pass
  
  labels = ('file', 'P', 'N', 'NET', 'SENTIMENT')
  df = pd.DataFrame.from_records(totallist, columns = labels)
  
  df.to_csv('oursentiment.csv', index = False)
  
  
  #print dict_p
  
  
  # allbigrams.append(ngram_list(file,2))
          # print i + ' BIGRAM - OK'
          # alltrigrams.append(ngram_list(file,3))
          # print i + ' TRIGRAM - OK'
          # allfourgrams.append(ngram_list(file,4))
          # print i + ' FOURGRAM - OK'
          # allfivegrams.append(ngram_list(file,5))
          # print i + ' TRIGRAM - OK'
          # allsixgrams.append(ngram_list(file,6))
          # print i + ' SIXGRAM - OK'
          # allsevengrams.append(ngram_list(file,7))
          # print i + ' SEVENGRAM - OK'
          # alleightgrams.append(ngram_list(file,8))
          # print i + ' EIGHTGRAM - OK'