All right, everyone.
So the next step, which we are going to play on the Web and leave you video, well, that is a quarter
stemmer.
So let me put first from any tiki God -- quarter import quarter Stemler.
And let me create Cordish.
Time or object.
Like just assigning to some yes.
And let us apply this stemming on every single word, every level in this review.
So same like earlier, we can use the list comprehension.
So it will be a four letter word in review.
We'll be applying.
He is not stem and let just supply void.
Let me have signee to review.
So it will came out with the stamp poison of this individual, Tolkan.
All right.
So while we can wall load, become low and places all this place.
All right, now, before the plane, all those things to back off, word model, heavy, how dogging
Giani and Sol joining.
We are just going to use a very simple blank screen and will be a plain joint function here.
We can pass like a review.
And let me assign it to review.
And let us bring what is there inside The View now.
Wow, place, hardball, low place, so that is a highly clean was off your factual data.
All right.
So let's just summarize what we had done in this whole cleaning tax process.
First important is Topper's and we just make everything lower case, then Vios Lytic like a tokenization,
then remote does topless.
Then we applied for this stamping process on individual Forkan and then we just Giani and created a
full sentence because this is what we are going to supply dolar bag offers.
Martin, now Aldo's process, we have done it for the one single record.
Now we are going to apply and combine all those steps and we'll be applying on every single record.
So what we will do, we are going to create one function or we can just triple what every single record
and we can just keep ending into some other location.
Let's see some karplus the variable I created as a list and legacy or what?
Let's say for I mean, let's say range were how many total number of records are there in our regional
dataset?
That will be a ninety nine.
Ninety nine.
So we can just simply use LAN data.
Let me Rindy I and we'll process one by one individual reckless and we'll just skip it to.
Oops.
I was just making sure that everything is working fine.
All right, so let me move this plane to an.
Now, what were the first step we need we had just.
Started copy pasting everything from here.
So first things we need to remove all those non alphabetic character punctuation, mark digits and everything.
So let me just selected.
And let me remove all those outputs.
All right, let me I say need to review.
And now instead of veto, it will be.
Because we are going to process on individual records and we are recruiting on everything that records,
like I say.
So that is the first step.
Next, what we need.
We just made it lower case.
So let me selected.
After Lord Casey.
We split that data.
So that means a kind of tokenization video.
And after tokenization, we tried to create a.
Stamper's.
First Stopford removal and then Stamford's.
But I do think that first we need to find the Stamper's and like I say.
And then we can remove this stopwork because Dopplers are like bees root were only available.
And this Demming also does the same thing.
What what we can do.
We can just simply combine both of this trap.
Like us.
Tamang also.
And this.
Stock would be more low, so.
So what I'm going to do, I'm just going to selected one statement and let me keep it here.
Before that, let's create the object of this part of Stemler.
I don't think we redeclared every time to create this Polish tmr object so we can just create it outside.
Although not quite so.
It has been already created.
And instead of a returning word, we can just simply return the stock stem.
All right, so we got a little review last after stemming plusses and Limor Stoppers.
Next is we just need to Giani.
So let's just use this for joining us.
And then we are just going to appending.
So these are all leaning steps and we have already created this corpus.
And inside of Corpus, I'm just going to pending you.
So we'll get a list of all the reviews, you know, Karpas ready.
All right, let me, Lenny.
And like discipline, this corpus.
All right.
So you can see every single reviews or I would say records in textual format.
That is all clean was one of our original texts and every single text, pre processing step has been
applied to all those records or the reviews.
Like solid data got clean.
Next thing we are going to do is a feature engineering that is nothing but a bag of more modern.
We are going to create it.
And for that, we are going to use this Haskell and Library Ciresi into Xingdou.