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Solve Part 2: Problem 3

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Alexander Hess 2022-08-05 09:36:26 +02:00
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# -*- 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 = "/home/alexander/repos/whu-textual-analysis/exam/part2_problems2n3/"
# =============================================================================
# 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("Question_[0-9]+:", input_text_question)
question_list = [x.strip() for x in question_list]
# Check whether there are empty questions, if so remove them
while question_list.count("") > 0:
question_list.remove("")
# get the total number of questions
number_questions = len(question_list)
# Split the text into individual answers
answer_list = re.split("Answer_[0-9]+:", input_text_answer)
answer_list = [x.strip() for x in answer_list]
# Check whether there are empty questions, if so remove them
while answer_list.count("") > 0:
answer_list.remove("")
# search for the term market/markets in each analyst question
# iterate over the list of questions
for j in range(number_questions):
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 = question_list[j]
# 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
question_list_of_words = re.split("\W{1,}", question_text)
# Are there upper case letters? Are there lower case letters?
# Remember to use a consistent format of the text and the search term.
# USE A SET FOR FASTER SEARCH
question_set_of_words = set(x.lower() for x in question_list_of_words)
if "market" in question_set_of_words or "markets" in question_set_of_words:
# it is a market-related question
market_question_count += 1
# 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 = answer_list[j]
# 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 = market_question_count / number_questions
# 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 = sent_tokenize(answers_market_questions)
# iterate all sentences
for i in range(len(list_sentences)):
# 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 = list_sentences[i]
# remove numbers (all kinds of forms)
sentence = re.sub("\$\d[\.,]\d", " ", sentence)
sentence = re.sub("\$\d", " ", sentence)
sentence = re.sub("\d[\.,]\d", " ", sentence)
sentence = re.sub("\d[$%]", " ", sentence)
sentence = re.sub("\d", " ", sentence)
# delete single letter words
sentence = re.sub(r"(?:^| )\w(?:$| )", " ", sentence).strip()
# remove subsequent whitespace
sentence = re.sub("\s{1,}", " ", sentence)
# split the sentence into words
list_of_words = re.split("\W{1,}", sentence)
# remove empty elements from the list of words
while list_of_words.count("") > 0:
list_of_words.remove("")
# remove stopwords
list_of_nonstop_words = []
for word in list_of_words:
if word not in stopwords.words():
list_of_nonstop_words.append(word)
# go over all potential three word combinations in the sentence.
# check whether you have at least three words remaining in the sentence.
if len(list_of_nonstop_words) >= 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 n in range(len(list_of_nonstop_words) - 2):
# append the three words of the trigram to the list of trigrams
# put a single whitespace between the three single words.
trigram_list.append(
list_of_nonstop_words[n]
+ " "
+ list_of_nonstop_words[n + 1]
+ " "
+ list_of_nonstop_words[n + 2]
)
# add the list of trigrams to the counter of trigrams
trigram_counter = collections.Counter(trigram_list)
# Get the 30 most frequent trigrams
top_30_trigrams = trigram_counter.most_common(30)
# 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(1, 31):
output_csv_file_3b.write(
str(i)
+ ";"
+ str(top_30_trigrams[i - 1][0])
+ ";"
+ str(top_30_trigrams[i - 1][1])
+ "\n"
)
# close files
output_csv_file_3b.close()
print("Part b) of the Problem has also been completed.")