Hello and welcome to this art tutorial.
So now, in this new step of the cleaning process,
we will remove all the punctuations in the reviews of the corpus.
And it's going to be as simple as before.
We will copy this line, paste
it below and instead of remove numbers here
we will input remove punctuation.
As simple as that.
We don't need anything else.
And that's ready to remove any punctuation and all the reviews of the corpus.
All right so let's check it out.
We can actually check it out with the first review.
Well it's really all that love this place.
And so what we're supposed to obtain after applying this remove
punctuation function and all the reviews of the corporate through the tmp function.
Well, the three little dots are supposed to disappear.
All right. So let's check it out.
So remember in the actual version of the corpus right now,
the first review is wow, thrilled that I love this place.
So now let's select this new line of code,
execute new corpus of reviews created with all the punctuation removed.
So let's go back to the console and let's press the up arrow to get
the line of code that is giving us access to the first review.
Here it is. So let's press enter now.
And as you can see, the thrilled that's disappeared.
Therefore that's exactly what the remove punctuation function does here.
It removes any kind of punctuation, including dot commas.
Collins.
Semicolon or any other kind of punctuation.
All right.
So next step done.
We are ready to move on to the next step,
which will be to remove all the non relevant words in the reviews.
So for example if we have a look at this first review here.
Well this is not very relevant.
You know this doesn't give any hint on knowing whether the review is positive
or negative.
So this is typically a word that we don't want to have in the final sparse matrix
because this is not relevant.
So we will remove it in the next step of the cleaning process.
And we'll do the same for all the other words of the same kind.
So let's do that in the next tutorial.
And until then enjoy machine learning.