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Classification of Yelp Reviews using Sentiment Analysis

IntermediateGuided Project

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.

4.6 (44 Reviews)

Language

  • English

Topic

  • Text Analytics

Enrollment Count

  • 649

Skills You Will Learn

  • Sentiment Analysis, Data Science, Embeddable AI, Python, Machine Learning

Offered By

  • IBM

Estimated Effort

  • 1 hour

Platform

  • SkillsNetwork

Last Update

  • March 27, 2024
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

Instructors

Svitlana Kramar

Data Scientist

I’m a passionate data science educator whose goal is to learn by teaching innovative data science tools that can improve our day-to-day tasks and our quality of life. My interests are in Natural Language Processing: text classification, summarization, and generation. Research can take a long time because there are a lot of resources and new opinions posted every day. Having tools to summarize and extract the information can save a lot of time. I hope we can all learn, approve, and apply the data science tools to cut down on the repetitive and tedious tasks, to make more informed decisions in life, to differentiate fake from real, and to open communication spaces to language-diverse or hearing-impaired audiences. The applications are limitless! My personality: I am a foodie and I love cooking and learning different cuisines. I also love travelling and connecting with people by learning a little bit of their language, about their food and music. I hold Data Science and Analytics master’s degree, specializing in Machine Learning, from University of Calgary.

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Contributors

Kathy An

Skills Network Data Scientist Intern

Skills Network Data Scientist Intern

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David Pasternak

Skills Network Data Scientist Intern

As early as I could remember, I was obsessed with figuring out how things work. Unfortunately for my parents, this often meant taking things apart to see what was inside and not being able to put it back together again, or putting copper wires into electrical outlets to see what would happen (to spare you from trying it yourself, it turns out the result is very bright and loud). This sometimes dangerous curiosity would eventually turn into a passion for physics which I would go on to study at the University of Toronto. I loved the process of gathering data, analyzing it, looking for patterns, and coming to a conclusion. I soon discovered that much of the procedures used to understand our physical universe follow a similar pattern in other aspects and domains of our world. This led me into the path of data science, where we could leverage our curiosity, analytical skillset, and love of discovery to come up with solutions to real world problems. I was hooked and I knew this joy of discovery is best shared. This put me on a mission to make the wonders of data science available to anyone and everyone who wished to learn it, thankfully made all the more accessible with the meteoric rise of online learning.

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