All right, everyone.
So let's continue our discussion on tax summarization.
And in this video, first, we are going to create those award frequency nets from this particular article.
How many times each individual would appear?
So let's say one, someone will appear.
How many times my my will appear.
How many times.
So this movie are going to create one dictionary, which will hold up all those keys as an individual
words and evaluates those.
Counter that.
How many times those particular one appear.
So let me create one dictionary object.
A blank look snoddy object.
That will be a word.
Let's say.
And let me try to post all those over in our dark object.
All right.
Let me bring all those who were.
All right.
So these are the words we're going to work up on.
Yes, that is starting from Maria Sarovar.
Now, first thing, what we are going to do, we just try to find better tools for appearing stop wordlist
order, punctuation, mark or not.
If it doesn't appear, then only we'll go ahead for further processing because as it is appear as a
part of contrition, Mark or a stop, or at least we just need to remove it and we are not going to
proceed ahead for further processing of those particular token.
So if.
Let's say were not tax, not lower.
Knocking, let's say stoppers, so we can create here the top wordlist from whatever we have imported
earlier, so there will be a stop worse.
Let me assign you two words as a list.
So if the Lord was in attacks, norteño stoppers.
Same like if or not tax, not lower.
Uniting punctuation mark.
Only will go ahead for further processing.
So if those particular work is not a part of stoppers punctuation mark, we are going up in, those
were in a word frequency count.
So, again, we'll just try to find.
Let me put into context.
As of now and let's see whether it is able to remove all those stopwork and a contusion mark or not.
All right, so you can see Maria Sharapova.
There is no dog that is no Colma.
And you can see everything like limo loading coal, my.
I mean, that single quotes leading removed.
So you can see that is not a part of, I think, contrition, maybe this kind of characters.
All right.
Let's not put much effort to update our punctuation.
Mark, as of now, has an exercise.
You can always go ahead and do that next days.
Again, we'll try to find whether those particular were already existing over frequency or not.
So first time, obviously, it will be a blank word frequency count.
But as in, when you get fill up all those words frequency, it may happen that you are in contact somewhere.
That is only a part of this word frequency.
So if this word, not text, not in a word frequency, not keys, that is not a part of keys.
So in that case, you can just add this word frequency key.
Whoops.
Or not.
Tax is equal to one.
Else.
What we're going to do if it already appears, we can just simply update the calendar.
So what happens here?
Suppose this Maria appears two times in this whole article.
So the first time it encounters that.
That is not a stop, or at least that is not a punctuation mark.
Let's go.
It is not even a part of a word frequency keys.
So in that case, we'll just add it to the word frequency list.
Having a key media and its value will be one moment, the next time this Maria appears again.
So again, it is not a stopper.
It is not a contrition map.
But now in this case, it is a part of this case.
So what happened that earlier?
We have only assigned this.
Maria is one.
Now we are going to put this down to the four keys.
Maria, you are going to add one more.
So now, Maria, become two.
And if I just execute it and let us bring this the word.
Frequency.
You can see this place appears one day, Murray appears mundane side up or someone time, basically
when times ten is six times.
Little six time.
All right.
So we got every single unique word exist in this article and has been assigned to some sort of counter
that.
How many times in that particular article and those four appear.
Let's try to find what is the maximum values.
So we can just simply grab it from max function that is in will functional pattern.
So word frequency.
Large values.
And you can see it's six.
Let me assign it to Max Sanders called Frequency.
So as the maximum frequencies six, let's try to normalize all those scores.
So what we can do?
We can just simply divide every single values by the six.
So let us agree to what all those words, in a word, frequency keys.
And word frequency.
What we are just going to divide it by.
Max Sanders, good frequency.
And now, if you tried to bring.
Widen and discord frequency.
All school will be normalized, so one become zero point one six six.
So now we go to school for every single word.
I would say a unique word appearing, not practical.
Next thing is this particular school we are going to apply on the individual sentence.
And we are trying to find the score of each sentences.
So those thing, we will see the next video.