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 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 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

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