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.
NLP: Twitter Sentiment Analysis
NLP: Twitter Sentiment Analysis
Instructor: Ryan Ahmed
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Learn, practice, and apply job-ready skills with expert guidance
4.6
(370 reviews)
Beginner level
Recommended experience
2 hours
Learn at your own pace
Hands-on learning
What you'll learn
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
Skills you'll practice
- Data Transformation
- Applied Machine Learning
- Machine Learning
- Scikit Learn (Machine Learning Library)
- Data Analysis
- Supervised Learning
- Data Processing
- Statistical Modeling
- Exploratory Data Analysis
- Natural Language Processing
- Data Cleansing
- Python Programming
- Text Mining
- Probability & Statistics
- Pandas (Python Package)
Details to know
Shareable certificate
Add to your LinkedIn profile
Taught in English
No downloads or installation required
Only available on desktop
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About this Guided Project
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
Recommended experience
Basic python programming and mathematics.
4 project images
Instructor
Instructor ratings
4.5 (21 ratings)Offered by
How you'll learn
Skill-based, hands-on learning
Practice new skills by completing job-related tasks.
Expert guidance
Follow along with pre-recorded videos from experts using a unique side-by-side interface.
No downloads or installation required
Access the tools and resources you need in a pre-configured cloud workspace.
Available only on desktop
This Guided Project is designed for laptops or desktop computers with a reliable Internet connection, not mobile devices.
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Showing 3 of 370
TP
5
Reviewed on Sep 15, 2020
Awesome course. The instructor is awesome in explaining all the details very well
SF
5
Reviewed on Oct 13, 2023
The course is great. I learned a lot. Each step is very well explained by the teacher.
YP
5
Reviewed on Sep 26, 2020
I would love to do more project under the guidance of the this professor.Each and every concept was clearly explained Thank You
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