[MUSIC] Build a sentiment classifier. And I'm gonna put a # here
to make it a nice title. So this is our next task here. And so,
when you build a sentiment classifier, you're talking about positives and
negatives. So, thumbs down, and thumbs up. But if you remember, our product ratings,
we're not about positive and negative, they're numerical things. So, for example,
if I take all the products, and I'll take the rating column and
I do a .show, and we did above just for the giraffe, but do a .show for
everything with the view equal to Categorical,
we're now getting a histogram for all of the views, and if you take a quick
look at it, you'll see that most reviews are positive across the board
107,000 reviews are five stars. So most people review positively, and just
write reviews about products they like. They don't typically write reviews
about products they don't like. Then the next set of reviews,
33,000 four stars. Then 3 stars and again, a lot of people
write really bad reviews 1 star, 2 star, why would you give
a review product 2 stars? You might as well just give them
1 star if you really hated it. And this is what we
observe in the histogram. But again, for sentiment analysis,
we have to define what's thumbs up and what's thumbs down. And so
I'm gonna make an arbitrary choice here. Let's say that things that 4,
5 stars are things that people liked. So those are positives. Things that 1 and 2 stars are negative. But the things that are 3 stars,
those are kind of in the middle. So, let's just throw those out. So we're gonna do a little bit of
what we'll call data engineering, just defining what is a positive and
negative sentiment. So let's do that right now. So in the subsection we're gonna define what's a positive and
a negative sentiment. And what I'm gonna do first is ignore all city star reviews. The way to do it is by saying, okay I'm gonna take the product
stable the variable products. And I'm gonna just select everything out of the products table
whose rating was not 3. So, products[products['rating'] |= 3]. So that was our first step in our
little data engineering task. And now, the next step in this task is to actually find what stems up and
stems down. So I'm gonna say a positive sentiment equals 4 star or 5 star reviews. So let's go ahead and add a new column to our table that
defines the actual sentiment. So products new column called sentiment. It's gonna be a binary column, zero one. And, the way we're gonna define
the column, is we're gonna say, of the products rating,
is it greater equal to 4? If it's greater or equal to 4,
it's gonna get a 1, and if it's less than 4,
it's gonna get a 0, and so now if I take another look
at my product stable, so head You should see that I've added a new
column on the right called Sentiment. And most of the sentiments are positive,
as we saw earlier, but many are negative, zero. Okay, now we're ready, we're finally ready to train our sentiment classifier. [MUSIC]