Offered By: IBM
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.
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440 EnrolledAt a Glance
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.
Learn by Doing
A Look at the Project Ahead
- 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
- Basic Python knowledge
Instructor
Estimated Effort
1 hour
Level
Intermediate
Skills You Will Learn
Python, Data Science, Machine Learning, Sentiment Analysis, Embeddable AI
Language
English
Instructors
Contributors
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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Skills Network Data Scientist Intern
Skills Network Data Scientist Intern
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