Byte-Sized-Chunks: Twitter Sentiment Analysis (in Python)

About Course

•  

  Sentiment Analysis (or) Opinion Mining is a field of NLP that deals with extracting subjective information (positive/negative, like/dislike, emotions).

  • Learn why it's useful and how to approach the problem: Both Rule-Based and ML-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.

  Sentiment Analysis:

  • Why it's useful,
  • Approaches to solving - Rule-Based , ML-Based
  • Training & Feature Extraction
  • Sentiment Lexicons
  • Regular Expressions
  • Twitter API
  • Sentiment Analysis of Tweets with Python

Curriculam

• Section 1: What Are You Feeling Like?
  	Lecture 1	You, This Course, and Us!	01:24
  	Lecture 2	A Sneak Peek at what's coming up	02:36
  	Lecture 3	Sentiment Analysis - What's all the fuss about?	17:17
  	Lecture 4	ML Solutions for Sentiment Analysis - the devil is in the details	19:57
  	Lecture 5	Sentiment Lexicons ( with an introduction to WordNet and SentiWordNet)	18:49
  	Lecture 6	Installing Python - Anaconda and Pip	09:00
  	Lecture 7	Back to Basics : Numpy in Python	18:05
  	Lecture 8	Back to Basics : Numpy and Scipy in Python	14:19
  	Lecture 9	Regular Expressions	17:53
  	Lecture 10	Regular Expressions in Python	05:41
  	Lecture 11	Put it to work : Twitter Sentiment Analysis	17:48
  	Lecture 12	Twitter Sentiment Analysis - Work the API	20:00
  	Lecture 13	Twitter Sentiment Analysis - Regular Expressions for Preprocessing	12:24
  	Lecture 14	Twitter Sentiment Analysis - Naive Bayes, SVM and Sentiwordnet	19:40

What you will get from this course?

• • 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

Who should buy this course?

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  • 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

  What are the requirements?

  • No prerequisites, 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