Grammatical Parsing Boosters

In order to detect those words related to Boosters, Grammatical Dependency Analysis was conducted using teh Stanford Grammatical Dependency Parser. The script was written in Python. Please find the sample code below (as comment)

Preparation

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

#os.chdir("/Users/lisaherzog/Google Drive/UM/Smart Services/Thesis/Thesis/Code/Feature Set4/Input")

1. Importing the dataset

#from pandas import ExcelWriter
#from pandas import ExcelFile

#Data = pd.read_excel('2. Booster Fragments.xlsx')

#Text =Data['Boost.Text'].tolist()

2. Tokenize Text into Sentences

First the text was tokenized into word tokens.

#Sent_Text = []

#for i in range(0,516):
    #txt = Text[i]
    #tokenized = sent_tokenize(txt)
    #Sent_Text.append(tokenized)

#Sent_Text[0:5]

3. Grammatical Parsing

Following, the parser was used to detect grammatical dependency.

#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)

#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)