alexander/whu-textual-analysis
1
0
Fork 0
Code Activity

Add the files for the take-home exam

Browse source
This commit is contained in:
Alexander Hess 2022-08-05 00:08:32 +02:00
commit 0d654bda9d
Signed by: alexander
GPG key ID: 344EA5AB10D868E0
248 changed files with 102406 additions and 0 deletions
Download patch file Download diff file Expand all files Collapse all files

216
exam/original-files/Problem_3_template.py Normal file
View file

@ -0,0 +1,216 @@
# -*- coding: utf-8 -*-
"""
Created on Sun Jul 31 14:37:49 2022
@author: Alexander Hillert, Goethe University
"""
# import packages
import re
import nltk
import collections
# define working directory
# adjust it to your computer
directory = "YOUR DIRECTORY"
# =============================================================================
# Part A: Identifying the answers to market-related sentences
# =============================================================================
# Create output file
output_csv_file_3a = open(directory+'Problem_3a_Market-related_Questions.csv', 'w', encoding="utf-8")
# Write variable names to the first line of the output file
# 1) Call-ID
# 2) Number of questions in the call
# 3) The number of market-related questions
# 4) The percentage of market-related questions
output_csv_file_3a.write('Call_ID;Number_Questions;Number_Market_Questions;Percetage_Market_Questions\n')
# create a text variable to store managers answers to market-related questions
answers_market_questions=""
# Iterate over the 60 questions and answer files respectively
for i in range(1, 61):
# If the execution of your scripts takes some time, printing the iterator
# gives you an impression of the overall progress
print(str(i))
# reset variables
market_question_count=0
# Open the ith question file
# IF YOU HAVE PROBLEMS OPENING THE FILES DOUBLE-CHECK THE DIRECTORY AND FOLDER NAME
input_file_question = open(directory+'/Problem_2_3_Sample_QandA/'+str(i)+'_questions.txt', 'r',
encoding='utf-8', errors='ignore')
# read the text from the question file
input_text_question=input_file_question.read()
# To identify managements' answer to a market-related question, also open the
# answer files and create a list of the individual answers.
# the jth list element in the answer list will correspond to the jth list
# element in the question list.
# Open the ith answer file
input_file_answer = open(directory+'/Problem_2_3_Sample_QandA/'+str(i)+'_answers.txt', 'r',
encoding='utf-8', errors='ignore')
input_text_answer=input_file_answer.read()
# Split the text into individual questions
question_list = re.split(TO BE COMPLETED, text_questions)
# Check whether there are empty questions, if so remove them
while TO BE COMPLETED > 0:
TO BE COMPLETED
# get the total number of questions
number_questions=TO BE COMPLETED
# Split the text into individual answers
answer_list = re.split(TO BE COMPLETED, input_text_answer)
# Check whether there are empty questions, if so remove them
while TO BE COMPLETED > 0:
TO BE COMPLETED
# search for the term market/markets in each analyst question
# iterate over the list of questions
for j in range(TO BE COMPLETED):
question_id=j+1
# it might be helpful to get the text of a question to a new variable
# of course, you can also work with the jth element of the question list.
question_text=TO BE COMPLETED
# search for market/markets in the list of words
# remember that searching for a word in a text is NOT the same as searching
# for a word in a list. Make sure that you only count actual matches!!!
# ADD necessary commands here
YOU MAY NEED TO ADD A COMMAND HERE.
# Are there upper case letters? Are there lower case letters?
# Remember to use a consistent format of the text and the search term.
if TO BE COMPLETED.count("market")>0 or TO BE COMPLETED:
# it is a market-related question
market_question_count=TO BE COMPLETED
# For Part b) you need the text of the answers to market-related
# questions. So, we identify the corresponding answer.
# question j relates to answer j.
# --> pick the right element from the answer list
market_answer=TO BE COMPLETED
# add the text of answer j to the total text of all answers
answers_market_questions=answers_market_questions+"\n"+market_answer
# compute the percentage of market-related questions
pct_mkt_questions=TO BE COMPLETED
# Write the call-ID, the total number of questions, the number of market questions,
# and the percentage of market questions to the output file
output_csv_file_3a.write(str(i)+';'+str(number_questions)+';'+str(market_question_count)+';' +
str(pct_mkt_questions)+'\n')
# close files
output_csv_file_3a.close()
print("Part a) of Problem 3 completed.")
# =============================================================================
# Part B: Most frequent trigrams in the answers to market-related questions
# =============================================================================
# import english stopwords
nltk.download('stopwords')
from nltk.corpus import stopwords
NLTK_stop_words=set(stopwords.words('english'))
# import sentence tokenizer
# even though we discussed the weaknesses of the tokenizer in class, for this
# text corpus it is fine to use the tokenizer.
nltk.download('punkt')
from nltk.tokenize import sent_tokenize
# list and counter for building trigrams
trigram_list=[]
trigram_counter=collections.Counter()
# Create output file
output_csv_file_3b = open(directory+'Problem_3b_Most_Frequent_Trigrams.csv', 'w', encoding="utf-8")
# Write variable names to the first line of the output file
# 1) rank of the trigram ranging from 1 to 30
# 2) trigram
# 3) frequency of the trigram
output_csv_file_3b.write('Rank;Trigram;Frequency\n')
# the managers' answers to market related sentences are stored in the text variable
# "answers_market_questions"
# split the entire answer text into single sentences
list_sentences=TO BE COMPLETED
# iterate all sentences
for i in range(TO BE COMPLETED):
# transform the ith sentence to lower or to upper case.
# make sure that the upper/lower case spelling is consistent with the
# stop word list!
sentence=TO BE COMPLETED
# remove numbers
sentence=TO BE COMPLETED
# delete single letter words
sentence=TO BE COMPLETED
# split the sentence into words
list_of_words=TO BE COMPLETED
# remove empty elements from the list of words
while TO BE COMPLETED>0:
TO BE COMPLETED
# remove stopwords
list_of_nonstop_words=[]
for TO BE COMPLETED:
if TO BE COMPLETED:
list_of_nonstop_words.append(TO BE COMPLETED)
# go over all potential three word combinations in the sentence.
# check whether you have at least three words remaining in the sentence.
if len(TO BE COMPLETED)>=3:
# go over all words in the sentence.
# remember to pay attention to the upper bound. For example, if there
# are 5 words in a sentence, you can only form 3 trigrams
for TO BE COMPLETED:
# append the three words of the trigram to the list of trigrams
# put a single whitespace between the three single words.
trigram_list.append(TO BE COMPLETED+' '+TO BE COMPLETED+' '+TO BE COMPLETED)
# add the list of trigrams to the counter of trigrams
trigram_counter=collections.Counter(TO BE COMPLETED)
# Get the 30 most frequent trigrams
top_30_trigrams=TO BE COMPLETED
# Write the 30 most frequent trigrams to the csv file.
# Remember Python starts counting at 0, while humans start at 1.
# So, the most frequent word (rank 1 in human counting) is element 0 for Python.
# Consequently, to get a consistent table, we must use the value i for the rank
# but call the element i-1.
for i in range(TO BE COMPLETED):
output_csv_file_3b.write(str(i)+";"+str(TO BE COMPLETED)+";"+\
str(TO BE COMPLETED)+"\n")
# close files
output_csv_file_3b.close()
print("Part b) of the Problem has also been completed.")