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




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

Note: This course is a subset of our 20+ hour course 'From 0 to 1: Machine Learning & Natural Language Processing' so please don't sign up for both:-)

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

Use Python and the Twitter API to build your own sentiment analyzer!

Url: View Details

What you will 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

Rating: 4.15

Level: All Levels

Duration: 3.5 hours

Instructor: Loony Corn


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