Parsing Text Files - Text Pre-Processing - Engineering Assignment Help

Assingment Task :

Task 1: Parsing Text Files (%55) 

This assessment touches the very first step of analyzing textual data, i.e., extracting data from semi-structured text files. Each student is provided with a data-set that contains information about COVID-19 related tweets (please find your own directory “part1” from here). Each text file contains information about the tweets, i.e., “id”, “text”, and “created_at” attributes. Your task is to extract the data and transform the data into the XML format with the following elements: 

1. id: is a 19-digit number.

2. text: is the actual tweet.

3. Created_at: is the date and time that the tweet was created 

Task 2: Text Pre-Processing (%45) 

This assessment touches on the next step of analyzing textual data, i.e., converting the extracted data into a proper format. In this assessment, you are required to write Python code to preprocess a set of tweets and convert them into numerical representations (which are suitable for input into recommender-systems/ information-retrieval algorithms). 

The data-set that we provide contains 80+ days of COVID-19 related tweets (from late March to mid July 2020). Please find your .xlsx file from the folder “part2” from this link. The excel file contains 80+ sheets where each sheet contains 2000 tweets. Your task is to extract and transform the information of the excel file performing the following task: 

1. Generate the corpus vocabulary with the same structure as sample_vocab.txt. Please note that the vocabulary must be sorted alphabetically.

2. For each day (i.e., sheet in your excel file), calculate the top 100 frequent unigram and top-100 frequent bigrams according to the structure of the sample_100uni.txt and sample_100bi.txt. If you have less than 100 bigrams for a particular day, just include the top-n bigrams for that day (n<100).

3. Generate the sparse representation (i.e., doc-term matrix) of the excel file according to the structure of the sample_countVec.txt 

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