# -*- 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.")