Hello everyone and welcome to the natural language processing project overview lecture and this lecture
we're just going to be getting a brief overview of the project notebook for this project.
You're going to be using natural language processing and then attempt to classify Yelp reviews into
one star or five star categories based off of the text content in the reviews is going to be a bit simpler
of a procedure than the lecture because we're going to fully utilize the pipeline methods for more complex
tasks.
Let me go ahead and walk through the notebook and I'll jump to it now.
OK.
Here I am at the project notebook.
So the natural language processing project what you're going to go ahead and do is use this yelper data
.
Each observation this data set is just a review of a particular business by a particular user.
It has the stars column which is the number of stars for the rating.
So they're one through five is signed by the reviewer to the business.
So the higher the stars the better the user thinks the businesses.
And in other words it's the rating of the business by the person who wrote the review.
There's also a cool column and it's the number of cool notes this review received from other Yelp users
.
That's an option and all reviews start with zero cool votes and there's no limit to how many cool votes
a reviewer can receive.
And basically in other words it's a rating of the review itself not a rating of the business.
Other users can say oh this review is cool.
And then the useful and funny columns are also similar to the cool column.
Users can come in.
See a review and rate it as either useful or funny or cool.
And then to get started you're going to follow the directions below you'll do the imports grab the data
a little bit of exploratory data analysis with some visualizations using Seaborn you'll do some more
visualization and then you'll come to an LP classification task where you're going to go ahead and use
sikat learn count vectorization train to split full time no meal and they've Beys modeling etc..
Some predictions and evaluations and that's it.
All right.
Hope you enjoy this project in the next lecture.
We'll go over the solutions to this project.
Thanks everyone and I'll see at the next lecture