Live training

Sentiment Analysis and Prediction in Python

In this live training, you will build a machine learning model to predict the sentiment of a review using the contents of the review. We will walk through all steps of the machine learning process, from importing the text data, tokenizing and vectorizing the text samples up to training a classifier and evaluating its performance.

Tuesday, July 19, 11 AM ET
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Python

What will I learn?

You will learn how to:

  • Load in and inspect a csv file with Python
  • Tokenize and vectorize text data to build numerical features for prediction.
  • Build a classifier that can predict the sentiment of a review.
  • Calculate and assess the performance of a classifier

What should I prepare?

We will be using DataCamp Workspace. Other than a DataCamp account, you don’t need anything. If you are part of specific enterprise groups, you may not yet be able to use DataCamp Workspace. You can create a free DataCamp account with your personal email address to follow along.

Who should attend?

In this live training, you will build a machine learning model to predict the sentiment of a review using the contents of the review. We will walk through all steps of the machine learning process, from importing the text data, tokenizing and vectorizing the text samples up to training a classifier and evaluating its performance. Justin will provide a template workspace for you so you can easily follow along as he’s coding up the model. We recommend that you have taken the following course before attending:

  • Intermediate Python
  • Introduction to Natural Language Processing in Python
  • Sentiment Analysis in Python

Presenter Bio

Justin Saddlemyer Headshot

Justin Saddlemyer

Workspace Architect

Justin is a Workspace Architect at DataCamp. He holds a bachelor's degree in psychology from St. Francis Xavier University and a graduate degree in social psychology from VU Amsterdam. In 2016 Justin received a Ph.D. in marketing from KU Leuven.
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