Machine Learning – Twitter Sentiment Analysis in Python


Use Python & the Twitter API to Build Your Own Sentiment Analyzer.

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.   Supplemental Material included!

Length: 3 hrs 30 min

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Learn why Sentiment Analysis is useful and how to approach the problem using both Rule-Based and Machine Learning-Based approaches. The details are really important – training data and feature extraction are critical. Sentiment Lexicons provide us with lists of words in different sentiment categories that we can use for building our feature set. All this is in the run up to a serious project to perform Twitter Sentiment Analysis. We’ll spend some time on Regular Expressions which are pretty handy to know as we’ll see in our code-along.

What Will I Learn?

  • Design and Implement a sentiment analysis measurement system in Python
  • Grasp the theory underlying sentiment analysis, and its relation to binary classification
  • Identify use-cases for sentiment analysis
  • Learn about Sentiment Lexicons, Regular Expressions & Twitter API

No prerequisites required, however knowledge of some undergraduate level mathematics would help, but is not mandatory. Working knowledge of Python would be helpful if you want to perform the coding exercise and understand the provided source code.

Who is the target audience?

  • Analytics professionals, modelers, big data professionals who haven’t had exposure to machine learning
  • Engineers who want to understand or learn machine learning and apply it to problems they are solving
  • Tech executives and investors who are interested in big data, machine learning or natural language processing
  • Product managers who want to have intelligent conversations with data scientists and engineers about machine learning

Sample clip


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

Janani Ravi, Vitthal Srinivasan, Swetha Kolalapudi, and Navdeep Singh have honed their tech expertise at Google and Flipkart. Together, they have created dozens of training courses and are excited to be sharing their content with eager students. The team believes it has distilled the instruction of complicated tech concepts into enjoyable, practical, and engaging courses.

Janani: 7 years at Google (New York, Singapore); Studied at Stanford; also worked at Flipkart and Microsoft

Vitthal: Also Google (Singapore) and studied at Stanford; Flipkart, Credit Suisse and INSEAD too

Swetha: Early Flipkart employee, IIM Ahmedabad and IIT Madras alum

Navdeep: Longtime Flipkart employee too, and IIT Guwahati alum