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