In Python 3.6

Write a program that computes the TF-IDF of a file of tweets. TF-IDF is a function that finds highly discriminating words or important words from a set of documents. (Formula for tfidf below)

Write a function that accepts a single word as a parameter, sets the word to lower case, and removes the following punctuation: .,->"<'

You must not clean text anywhere in the program than in this function.

A function that is passed two parameters: a word (a str), and a sentence (also a str). The function should return True if the word is in the sentence, and False if it is not.

Remember to clean the words in the sentence before comparing!

A function that is passed two parameters: a word (a str), and a sentence (also a str). The function should return True if the word is in the sentence, and False if it is not.

Remember to clean the words in the sentence before comparing!

A function that takes a list of tuples that was generated by the TF-IDF algorithm. The tuples will be a word, and the TF-IDF ranking of the word.

The function should return a sorted list, sorted from largest TF-IDF ranking to smallest.

The easiest implement a sorting function:

Create an empty list for sorted data.

Find the maximum value from the unsorted list

append the maximum value from #3 to the sorted list

Remove the value from #3 from the unsorted list

Repeat 2-4 until there are no values in the unsorted list

TF = freq(w) / total unique words

IDF = log(# of documents with word w/# of documents)

TFIDF = TF x IDF

Text frequency is defined by: The number of times the word w appears in all the documents, divided by the number of words in all the documents.