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Hello
my friends, and welcome to this new part.

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On Natural Language Processing.

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I'm super excited to start this part
because this is the branch of machine

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learning with which you can build
chat bots and machine translations.

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So of course, this is not

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what we're going to do in this part
because this is really advanced NLP.

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So we'll just cover the basics
with sentiment analysis, which consists

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of training a machine
to understand some text

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and predict
a certain outcome for this text.

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So in our case study here, these text
will be reviews of a restaurant.

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And we'll have to train a machine

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to understand
if each review is positive or negative.

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So very simple, very classic.

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But the best way to be introduced to NLP.

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All right.

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So before we start let's make sure
everyone here is on the same page.

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This is the folder containing
all the codes and data sets

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and of which I give you the link
right before this tutorial in the article.

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So make sure to connect to that link.

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And now there we go.

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We can and support seven
natural language processing.

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So in this part
you will only find one section.

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That's because we only do one case study
of NLP about sentiment analysis.

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However, you will see that you can try

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diverse machine
learning models to tackle the problem.

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Indeed, the essential

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part of our implementation
will be to build the bag of Words model.

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But then once it is built,
we can try several classification models.

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Why classification models?

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That's because we'll have to predict,
you know, a binary outcome 1 or 0 one,

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meaning the review is positive and zero,
meaning the review is negative.

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So you'll have actually the flexibility
to try several machinery models.

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And this will actually be
your final exercise of this section.

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So there we go. Let's do this.

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Let's enter section 36.

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Now natural language processing.

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And as usual we're going to start with
Python in which you will find two files.

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The implementation natural language
processing dot Ipynb,

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which you can open with either
Google Collaboratory or Jupyter Notebook.

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And our data set restaurant
reviews dot this time.

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Net CSV, but SVM
and this will be a good opportunity

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for me to train
you on how to import a TSV data set.

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TSV mean Tab separated value instead of
comma separated value like in a CSV.

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So basically the only difference

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is that in the previous data sets
we worked with, well,

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you know, the features and the dependent
variable were separated by commas.

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And in this one, well,
instead of being separated by commas,

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the reviews and the dependent variable
will be separated by a tab.

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And this makes sense, right?

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Because in the reviews
we already have commas

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and therefore they would create nonsense
features.

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But let me show you what this data set
looks like.

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So as you can see,

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there are only two columns,
the first one containing all the reviews.

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So for example, this is the first one.

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Well, not this place.

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A second one trust is not good.

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another one, great touch, etc..

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So you have in total let's see, 1000
reviews.

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Right.

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So we're going to train our machine
learning

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to actually understand text and predict
if the text are positive or negative.

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With 1000 texts.

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All right.

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And then the second column is of course
if the review is positive or negative.

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So one means that it is positive
meaning the customer liked it.

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And zero means
that the review is negative.

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And of course we have the real outcomes
in order to train our machine

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learning model to understand if each of
these text is positive or negative.

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So that's purely in the end,
you know, a classification problem.

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But the essential part of it is
that will train the machine to understand

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these text first and then to predict
if they are positive or negative.

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All right.

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So very simple case study
very simple data set.

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That means we are ready
to start the implementation

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of natural language processing.

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So as you prefer

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feel free to open it with either
Google Colaboratory or Jupyter Notebook.

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I'm opening it

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with Google Collab as usual,
so feel free to do the same if you'd like.

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And now the Notebook is loading.

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In a second it will be laying out
all right loading leading out.

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Perfect. And this is the implementation.

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And as usual this is in read only mode.

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And we want to re-implement
this from scratch.

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Therefore we're going

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to create a copy right away
so that we can modify the code inside.

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So we're going to click
save a Copy and drive here.

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This will create a copy,
after which we will be able

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to modify the code and re-implement
this from scratch.

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And speaking of re-implementing it
from scratch.

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Well, let's delete all the code cells
because we will re-implement them.

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So let's click this trash button here
and each of the code cells,

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but not the text, so that we can keep that
well highlighted structure

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and see where we're going
at each time of the implementation.

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All right. So almost done.

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It's actually an implementation
in about ten steps.

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But you will recognize
some of the steps as steps we did before.

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You'll see I'm going to show you
in a second.

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All right.

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So let's have a look at the structure
of this implementation.

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We will start first
by importing the libraries as usual.

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Because indeed we will need
several libraries to preprocess

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our texts and train our future machine
learning model.

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Then we will import the data set.

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So that's actually the data
preprocessing phase.

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But not only that, the data preprocessing

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phase will also contain the next two cells
cleaning the text.

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Indeed, we will have to simplify the text
as much as we can

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in order to ease the learning process
of the machine learning model.

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You know, we'll have to remove
all the punctuations.

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We'll have to put
all the letters in lowercase.

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Then we'll have to apply stemming.

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You know, we'll have to make
very clean text to alleviate

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the learning process of the future
classification model will build.

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So that's a compulsory process.

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When doing NLP
you have to preprocess the text basically.

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Then we'll create the bag of words model

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which is at the heart
of sentiment analysis.

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And then there you go.

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That's
where you will recognize everything.

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Once we have the bag of Words

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model, we basically have a data
set ready to be trained, right?

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We have a dataset ready
to be trained by a machine learning model.

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And that's why then we will just apply
the classic process of training a model.

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First, we will split the data
set into the training set and test it

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so that we can indeed
have a set where we train the model

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to understand text and predict
if the text are positive or negative,

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and the test set
so that we can evaluate the performance

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on mutex
on which the model wasn't trained.

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And then there we go, we train.

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So I chose a Naive Bayes
model on the training set.

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But you will see that your

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final exercise at the end will be to try
the other classification models

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and see if you can beat the accuracy
I will get in this implementation.

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Then we will predict the test result
and finally

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we will make the confusion matrix
and get the final accuracy.

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So that's our structure.

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That's our NLP journey.

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So now as soon as you're ready
let's start in the next tutorial

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with the simple data preprocessing phase.

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I can't wait to start.

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See you in the next tutorial
and until then, enjoy machine learning!