Sentiment Analysis, or Opinion Mining, is a field of Neuro-linguistic Programming that deals with extracting subjective information, like positive/negative, like/dislike, and emotional reactions. In this “Twitter Sentiment Analysis in Python” online course, you’ll learn real examples of why Sentiment Analysis is important and how to approach specific problems using Sentiment Analysis.
Course Length: 3.5 Hours
Chapter 01: What are You Feeling Like?
Lesson 01: Introduction: You, This Course & Us!
Lesson 02: Sentiment Analysis: What’s all the fuss about?
Lesson 03: Machine Learning Solutions for Sentiment Analysis: the devil is in the details
Lesson 04: Sentiment Lexicons (with an introduction to WordNet and SentiWordNet)
Lesson 05: Installing Python – Anaconda and Pip
Lesson 06: Back to Basics: Numpy in Python
Lesson 07: Back to Basics: Numpy & Scipy in Python
Lesson 08: Regular Expressions
Lesson 09: Regular Expressions in Python
Lesson 10: Put it to work: Twitter Sentiment Analysis
Lesson 11: Twitter Sentiment Analysis: Work the API
Lesson 12: Twitter Sentiment Analysis: Regular Expressions for Preprocessing
Lesson 13: Twitter Sentiment Analysis: Naive Bayes, SVM & SentiWordNet
Minimum specifications for the computer are:
Microsoft Windows XP, or later
Modern and up to date Browser (Internet Explorer 8 or later, Firefox, Chrome, Safari)
OSX/iOS 6 or later
Modern and up to date Browser (Firefox, Chrome, Safari)
Internet bandwidth of 1Mb or faster
Flash player or a browser with HTML5 video capabilities (We recommend Google Chrome)