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Classification of Yelp Reviews using Sentiment Analysis

Sentiment Analysis has become a very popular tool in extracting subjective information from the social media. It can help businesses to understand their brand, product or service better. In this Guided Project, you will be introduced to several Natural Language Processing Techniques to help you derive some meaning from yelp business reviews, as well as to build and test a classification model that can divide these reviews based on their polarities.

GPXX0UN5EN

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IntermediateGuided Project

Language

  • English

Topic

  • Text Analytics

Organization

  • IBM

Estimated Effort

  • 1 hour
About This Guided Project

Learn by Doing

A guided project is a hands-on tutorial designed to help you learn a particular technology by doing a real project. It includes step-by-step instructions with explanations, examples and exercises that can be followed and practiced in a lab environment. Hands-on skills are highly sought out by employers when determining job readiness. Guided projects are interactive, on-demand and will equip you with practical abilities that can be applied on the job!


A Look at the Project Ahead

Once you have completed this project, you'll be able to use the Python to: 
  • Explore yelp business reviews dataset to perform text cleaning, vectorization, and classification 
  • Use scikit-learn library tools to extract some meaning from the sentiments
  • Create a model to classify reviews based on their positive or negative  sentiments

What You'll Need

Before starting this project, it'll be helpful to have the following:
  • Basic Python knowledge

Instructor

Svitlana Kramar, IBM
Svitlana KramarAuthor
Data Science Intern. Working towards my Master's Degree in Data Science and Analytics at University of Calgary, Alberta
David PasternakAuthor
Kathy AnAuthor