NLP: Twitter Sentiment Analysis

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In this Guided Project, 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 hands-on project, we will train a Naive Bayes classifier to predict sentiment from thousands of Twitter tweets. This project 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 Programming
  • Machine Learning
  • Natural 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 Projects 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

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