Grammatical Parsing Negations

#import os
#import numpy as np
#import pandas as pd
#import nltk
#import re
#import string
#from nltk.tokenize import sent_tokenize
#nltk.download()

#os.chdir("/Users/lisaherzog/Google Drive/UM/Smart Services/Thesis/Thesis/Code/Feature Set3/Grammatical Parsing")

Import Data

#from pandas import ExcelWriter
#from pandas import ExcelFile

#Data = pd.read_excel('Negative Text Fragments.xlsx')
#Text =Data['Neg.Text'].tolist()

Tokenize Text into Word tokens

#Sent_Text = [None]*289

#for i in range(0,289):
    #txt = Text[i]
    #tokenized = sent_tokenize(txt)
    #Sent_Text.append(tokenized)

#Sent_Text = Sent_Text[289:578]

Import Parser

#from nltk.parse.stanford import StanfordDependencyParser
#path_to_jar = '/Users/lisaherzog/Google Drive/UM/Smart Services/Thesis/Thesis/Stanford Grammatical Parser/stanford-parser-full-2015-04-20/stanford-parser.jar'
#path_to_models_jar = '/Users/lisaherzog/Google Drive/UM/Smart Services/Thesis/Thesis/Stanford Grammatical Parser/stanford-parser-full-2015-04-20/stanford-parser-3.5.2-models.jar'
#dependency_parser = StanfordDependencyParser(path_to_jar=path_to_jar, path_to_models_jar=path_to_models_jar)

Perform Grammatical Parsing

#Reviews= []
#Length= len(Sent_Text)

#for i in range(0,Length):
    #Extract = Sent_Text[i]
    #Extract_Length=len(Extract)
    #dependencies = []
    #Reviews.append(dependencies)

    #for j in range(0,Extract_Length):
        #Sent_Extract = str(Extract[j])
        #result = dependency_parser.raw_parse(Sent_Extract)
        #dep = result.__next__()
        #resultList = list(dep.triples())
        #dependencies.append(resultList)

Negation Extraction

Extract only those reviews that have the relationship “neg”

#Negations= []
#Length= len(Reviews)

#for k in range(0,Length):
    #Extract = Reviews[k]
    #Extract_Length=len(Extract)
    #Sentences = []
    #Negations.append(Sentences)
    
    
    #for l in range(0,Extract_Length):
        #Sent_Extract = Extract[l]
        #Sent_Length=len(Sent_Extract)
        #Triples = []
        #Sentences.append(Triples)
 
#Store only those Triples containing negations       
        #for m in range(0,Sent_Length):
            #Triple = Sent_Extract[m]
            #Relation= Triple[1]
            #if(Relation=="neg"):
                #Word= Triple[0][0]
                #Result= [k,l,Word]
            #else:
                #Result= None
            #Triples.append(Result)

Add the Negation Tagg

#Review_IDs = []
#Sentence_IDs=[]
#Final_Negated_Fragment = []     
#Length_Final = len(Negations_Simplified)

#for r in range (0, Length_Final):
    #Extract = Negations_Simplified[r] # WORKS
    #Review_ID = Extract[0] # WORKS
    #Sentence_ID= Extract[1] # WORKS
    #Word = Extract[2] # WORKS
    #ADD= "_NOT"# WORKS
    #NEW_WORD= Word+'_NOT'# WORKS
    #Review_IDs.append(Review_ID)
    #Sentence_IDs.append(Sentence_ID)

    
    #Sentence = Sent_Text[Review_ID][Sentence_ID] #WORKS
    #New_Sentence = Sentence
    #New_Sentence = New_Sentence.replace(Word, NEW_WORD)
    #print(New_Sentence)#Works
    #Final_Negated_Fragment.append(New_Sentence)

```