NLP: Twitter Sentiment Analysis
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
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
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:
Import libraries and datasets
Perform Exploratory Data Analysis
Plot the word cloud
Perform data cleaning - removing punctuation
Perform data cleaning - remove stop words
Perform Count Vectorization (Tokenization)
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
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
Reviews
TOP REVIEWS FROM NLP: TWITTER SENTIMENT ANALYSIS
Overall nicely guided small project. The tutor explained it very well. Please make more such short courses. More concepts from Basic NLP can be covered.
I would love to do more project under the guidance of the this professor. Each and every concept was clearly explained\n\nThank You
This guided project is a chance to learn a basic technical skill in data cleaning. I love the explanation for Naive Bayes algorithm.
MemoryError: Unable to allocate 9.03 GiB for an array with shape (25569, 47386) and data type int64 Solve the issue
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