Twitter Sentiment Analysis Tutorial

4.6
stars
269 ratings
Offered By
Coursera Project Network
7,156 already enrolled
In this Guided Tutorial, you will:

Create a pipeline to remove stop-words, punctuation, and perform tokenization

Understand the theory and intuition behind Naive Bayes classifiers

Train a Naive Bayes Classifier and assess its performance

Clock2 hours
BeginnerBeginner
CloudNo download needed
VideoSplit-screen video
Comment DotsEnglish
LaptopDesktop only

In this guided tutorial, we will train a Naive Bayes classifier to predict sentiment from thousands of Twitter tweets. This tutorial could be practically used by any company with social media presence to automatically predict customer's sentiment (i.e.: whether their customers are happy or not). The process could be done automatically without having humans manually review thousands of tweets and customer reviews. Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

Skills you will develop

Artificial Intelligence (AI)Python ProgrammingMachine LearningNatural Language Processing

Learn step-by-step

In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:

  1. Import libraries and datasets

  2. Perform Exploratory Data Analysis

  3. Plot the word cloud

  4. Perform data cleaning - removing punctuation

  5. Perform data cleaning - remove stop words

  6. Perform Count Vectorization (Tokenization)

  7. Create a pipeline to remove stop-words, punctuation, and perform tokenization

  8. Understand the theory and intuition behind Naive Bayes classifiers

  9. Train a Naive Bayes Classifier

  10. Assess trained model performance

How Guided Tutorials work

Your workspace is a cloud desktop right in your browser, no download required

In a split-screen video, your instructor guides you step-by-step

Instructor

Reviews

TOP REVIEWS FROM TWITTER SENTIMENT ANALYSIS TUTORIAL

View all reviews

Frequently asked questions

Frequently Asked Questions

More questions? Visit the Learner Help Center.